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TD 225 
C43 
A4733 
2007 
Copyl 


United States Region III Region III EPA 903-R-07-003 

Environmental Protection Chesapeake Bay Water Protection CBP/TRS 285/07 

Agency Program Office Division July 2007 

In coordination with the Office of Water/Office of Science and Technology, Washington, D.C., and the states 
of Delaware, Maryland, New York, Pennsylvania, Virginia and West Virginia and the District of Columbia 


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Ambient Water Quality 
Criteria for Dissolved 
Oxygen, Water Clarity and 
Chlorophyll a for the 
Chesapeake Bay and Its 
Tidal Tributaries 

I JUh2* 20"3 

2007 Addendum \ 

^ TftA ^ ft*. 








Library of Congress 



2011 459396 











Library ft * Concuss 


Ambient Water Quality Criteria 
for Dissolved Oxygen, Water Clarity 
and Chlorophyll a for the Chesapeake Bay 

and Its Tidal Tributaries 

2007 Addendum 

July 2007 

U.S. Environmental Protection Agency 
Region III 

Chesapeake Bay Program Office 
Annapolis, Maryland 

and 

Region III 

Water Protection Division 
Philadelphia, Pennsylvania 

in coordination with 

Office of Water 

Office of Science and Technology 
Washington, D.C. 

and 

the states of 

Delaware, Maryland, New York, 

Pennsylvania, Virginia and 
West Virginia and the District of Columbia 


•* W 






Ill 


Contents 

Acknowledgments . v 

I. Introduction . 1 

Literature Cited. 3 

II. Refinements to the Chesapeake Bay Water Quality Criteria 

Assessment Methodology . 4 

Background. 4 

Overview of the CFD Assessment Methodology. 5 

Description and Evaluation of the CFD-Based Assessment Methodology . . 9 

Example CFD-based criteria assessment . 9 

CFD reference curves . 12 

Comparing assessment and reference curves . 15 

Development of a statistical decision-making framework . 16 

Results of the scientific evaluation . 19 

Application of the CFD-Based Assessment Methodology. 21 

Recommendations for application of the CFD-based methodology . 21 
Recommendations for future refinement of the 

CFD-based assessment methodology . 23 

Literature Cited. 24 

III. Application of Chesapeake Bay Water Quality Criteria 

Assessment Procedures . 25 

Background. 25 

Assessment Units, Segmentation, and Sub-Segmentation. 26 

Data to be Used in Chesapeake Bay Criteria Assessments . 28 

Updating the Criteria Attainment Assessment Framework . 31 

Literature Cited. 32 

IV. Refinements to the Chesapeake Bay Dissolved Oxygen Criteria 

Assessment Procedures . 33 

Background. 33 

Temporal Periods for Assessment of Dissolved Oxygen Criteria. 36 

Dissolved Oxygen Criteria Assessments in Shallow Waters 

Versus Open Waters. 37 

Assessment of Short Duration Dissolved Oxygen Criteria . 38 

Dissolved Oxygen Criteria Reference Curves . 39 


Contents 




























iv 


Summer open-water and deep-water . 39 

dissolved oxygen criteria reference curves . 42 

Non-summer open-water dissolved oxygen criteria 

reference curve . 42 

Assessment of deep-channel instantaneous minimum 

dissolved oxygen criteria . 42 

Use of Percent Saturation as Dissolved Oxygen Criteria . 43 

Literature Cited. 45 

V. Refinements to the Shallow-Water Designated 

Use Assessment Procedures . 47 

Background. 47 

Shallow-Water Designated Use Attainment Assessment . 48 

Assessment based on the single best year of SAV . 50 

Assessment based on water clarity acres . 53 

Assessment based on CFD-based water clarity criteria attainment . 56 

Shallow-Water Designated Uses and SAV No-Grow Zones . 56 

Water Clarity Criteria Reference Curves. 57 

Literature Cited. 59 

VI. Chlorophyll a Criteria Assessment Procedures . 61 

State Water Quality Standards . 61 

Chlorophyll a Criteria Assessment Procedures . 62 

Literature Cited. 62 

VII. Shallow-water Monitoring and Application 

for Criteria Attainment Assessment . 63 

Design and Approach for Chesapeake Bay Shallow-Water Monitoring . 63 

Shallow-water monitoring design . 65 

Continuous monitoring component . 66 

Water quality mapping component . 67 

Schedule for Assessment of Shallow-Water Designated Use Habitats . . 68 

Extending the timeframe . 69 

Additional resources . 69 

Assessment based on reduced monitoring . 70 

Segment prioritization schedule . 74 

Dissolved Oxygen Criteria Assessments Using 

Shallow-water Monitoring Data . 75 

Temporal standardization . 75 

Scaling and interpolation issues . 77 

Water Clarity Criteria Assessments Using 

Shallow-water Monitoring Data . 78 

Analysis issues . 79 

Statistical modeling . 79 

Interpolation . 80 

Chlorophyll a Criteria Assessments Using 

Shallow-water Monitoring Data . 82 

Statistical modeling . 83 

(i 


Contents 





































Modeling Approach . 84 

Analysis issues . 84 

Literature Cited. 88 

VIII. Framework for Chesapeake Bay Tidal Waters 303(d) 

List Decision-making . 89 

Background. 89 

Listing Category Decisions. 89 

Criteria attainment assessments . 90 

Dissolved oxygen criteria attainment assessments . 90 

Water clarity criteria attainment assessments . 91 

Chlorophyll a criteria attainment assessments . 91 

Benthic index of biotic integrity assessments . 92 

Assessment reporting framework . 92 

Listing Decision Framework . 95 

Segments previously listed as impaired . 96 

Segments not previously listed as impaired . 96 

Shallow-water designated use listing decisions . 96 

Literature Cited. 97 

Acronyms . 98 

Appendices 

A. The Cumulative Frequency Diagram Method for Determining Water 
Quality Attainment: Report of the Chesapeake Bay Program 

STAC Panel to Review Chesapeake Bay Program Analytical Tools . A-l 

B. Detailed Chesapeake Bay Water Quality Criteria Assessment 

Methodology .B-l 

C. Evaluation of Options for Spatial Interpolation .C-l 

D. User Guide and Documentation for the 

Chesapeake Bay Interpolator . D-l 

E. Potential Methods for Assessing Shorter Duration 

Dissolved Oxygen Criteria.E-l 

F. Data Used in Deriving the Open-Water, Deep-Water, and 

Deep-Channel Dissolved Oxygen Criteria Summer Biological 
Reference Curves .F-l 

G. Equations for the Open-Water, Deep-Water, and Deep-Channel 
Dissolved Oxygen Criteria Summer Biological Reference Curves . . G-l 

H. Equations for the Water Clarity Criteria Biological Reference 

Curves .H-l 

I. Evaluation of Maryland and Virginia Chesapeake Bay Segment SAV 

Acreages from 2003 to 2005 for Prioritizing Shallow-Water Monitoring 
by Segment . 1-1 




























VI 


J. Chesapeake Bay Estuarine Benthic Communities Assessment Protocol 


for Maryland and Virginia 305b/303d Integrated Reports.J-l 

K. 2006 303(d) Assessment Methods for Chesapeake Bay Benthos . . . . K-l 

L. Addendum to the Report: Development of Diagnostic Approaches to 

Determine Sources of Anthropogenic Stress Affecting Benthic 
Community Condition in the Chesapeake Bay .L-l 


Contents 







VII 


Acknowledgments 


This second addendum to the April 2003 Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity, and Chlorophyll a for Chesapeake Bay and Its 
Tidal Tributaries was developed and documented through the collaborative efforts of 
the members of the Chesapeake Bay Program’s Criteria Assessment Procedures 
Workgroup and Water Quality Steering Committee. 

PRINCIPAL AND CONTRIBUTING AUTHORS 

This document resulted from the collaborative expertise and talents of the 
Chesapeake Bay Program’s state agency, federal agency, and academic institutional 
partners. The 25 principal authors (listed first) and contributing authors (listed in 
alphabetical order) follow by chapter. Unless noted, author affiliations are listed 
under the specific workgroup or committee acknowledgments. Chapter 1: Richard 
Batiuk; Chapter 2: Steve Preston; Chapter 3: Steve Preston; Chapter 4: Richard 
Batiuk, David Jasinski, Marcia Olson, and Gary Shenk; Chapter 5; Richard Batiuk; 
Chapter 6: Elgin Perry, Richard Batiuk, and Larry Harding (University of Maryland 
Center for Environmental Science); Chapter 7: Bruce Michael, Rick Hoffman, Mary 
Ellen Ley, Ken Moore, Elgin Perry, and Mark Trice; Chapter 8: Larry Merrill, Mark 
Barath, Richard Batiuk, and Richard Eskin; Appendix A: David Secor; Mary 
Christman; Frank Curriero; David Jasinski; Steve Preston; Ken Reckhow; and Mark 
Trice; Appendix B: Gary Shenk; Appendix C: Steve Preston; Appendix D: Lowell 
Bahner (NOAA Chesapeake Bay Office), David Jasinski, and Gary Shenk; Appendix 
E: Gary Shenk, Marcia Olson, and Elgin Perry; Appendices F, G, and H; Gary 
Shenk; Appendix I: Bruce Michael; Appendix J: Mark Barath; Appendix K: Roberto 
Llanso (Versar), Dan Dauer (Old Dominion University), Mike Lane (Old Dominion 
University), and Jon Volstead (Versar); and Appendix L: Dan Dauer, Mike Lane, and 
Roberto Llanso. 

CRITERIA ASSESSMENT PROTOCOLS WORKGROUP 

Steve Preston, chair (U.S. Geological Survey/Chesapeake Bay Program Office), 
Harry Augustine (Virginia Department of Environmental Quality); Mark Barath 


Acknowledgments 



1 


chapter | 


Introduction 


In April 2003, the U.S. Environmental Protection Agency (EPA) published the Am¬ 
bient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a 
for the Chesapeake Bay and Its Tidal Tributaries (Regional Criteria Guidance) in 
cooperation with and on behalf of the six watershed states—New York, Pennsyl¬ 
vania, Maryland, Delaware, Virginia, and West Virginia—and the District of 
Columbia. The culmination of three years of work, the criteria document resulted 
directly from the collective contributions of hundreds of regional scientists, technical 
staff, and agency managers as well as the independent review by recognized scien¬ 
tific experts across the country (U.S. EPA 2003). 

In October 2004, EPA published the first addendum to the 2003 Regional Criteria 
Guidance (U.S. EPA 2004). The addendum provided additional guidance on: 

• Applying the temperature-based open-water dissolved oxygen criteria required 
to protect the endangered shortnose sturgeon; 

• Assessing attainment of the instantaneous minimum and 7-day mean dissolved 
oxygen criteria using monthly mean water quality monitoring data; 

• Deriving site-specific dissolved oxygen criteria and assessing criteria attain¬ 
ment of those tidal systems where the extensive adjacent tidal wetlands cause 
naturally low dissolved oxygen levels; 

• Delineating the upper and lower boundaries of the pycnocline that defines the 
vertical boundaries distinguishing open-water, deep-water, and deep-channel 
designated uses; 

• Applying, in combination, the numerical water clarity criteria to shallow water 
habitats and submerged aquatic vegetation restoration goal acreages for 
defining attainment of the shallow-water bay grass designated use; and 

• Determining where numerical chlorophyll a criteria should apply to local 
Chesapeake Bay and tidal tributary waters. 

From 2004 through early 2006, Delaware, Maryland, Virginia, and the District of 
Columbia adopted: the EPA-published Chesapeake Bay water quality criteria for 


chapter i 


Introduction 


2 


dissolved oxygen, water clarity, and chlorophyll a; the EPA-recommended tidal 
water designated uses; and the EPA-established criteria assessment procedures into 
their respective state water quality standards regulations. All four jurisdictions 1 
promulgated narrative chlorophyll a criteria in their standards regulations. Virginia 
promulgated numerical segment- and season-specific chlorophyll a criteria for the 
tidal James River. The District of Columbia promulgated numerical chlorophyll a 
criteria for its reach of the tidal Potomac River and its remaining tidal waters, having 
previously adopted numerical chlorophyll a criteria for protection of the tidal 
Anacostia River. 

The April 2003 Regional Criteria Guidance and the October 2004 addendum docu¬ 
ments published the criteria attainment assessment methods (U.S. EPA 2003, 2004). 
These methods characterize the spatial and temporal variability of the appropriate 
water quality parameters and provide a clear basis for deciding whether a criterion 
or set of criteria protecting a designated use in a specific segment of the mainstem 
Chesapeake Bay or one of the tidal tributaries or embayments were in attainment. 
The methods were quite detailed; however, specific technical and procedural issues 
remained in applying the methods as specified in the original publication by EPA 
from April 2003. These issues required resolution to allow Delaware, Maryland, 
Virginia, and the District of Columbia to assess attainment of their new Chesapeake 
Bay water quality standards regulations fully. 

This second addendum documents the revised, refined, and new criteria assessment 
methods for the published Chesapeake Bay dissolved oxygen, water clarity, and 
chlorophyll a criteria. 

• Chapter 2 documents refinements to and recommendations for further devel¬ 
opment of the spatial interpolation and statistical aspects of the overall 
Chesapeake Bay water quality criteria attainment assessment methodology. 

• Chapter 3 documents the resolution of and recommended procedures for 
addressing a series of overarching Chesapeake Bay water quality criteria 
assessment issues. 

• Chapter 4 documents refinements and additions to the procedures for 

assessing the previously published Chesapeake Bay dissolved oxygen criteria. 

• Chapter 5 documents refinements and additions to the procedures for 

assessing the previously published Chesapeake Bay water clarity criteria and 
determining attainment of the shallow-water bay grass designated use. 

• Chapter 6 documents refinements and additions to the procedures for 

assessing attainment of state-adopted numerical concentration-based chloro¬ 
phyll a criteria. 


'References throughout the text to “states” or “jurisdictions” means a collective reference to the states 
of Delaware and Maryland, the Commonwealth of Virginia, and the District of Columbia. All four have 
Chesapeake Bay tidal waters within their jurisdictional boundaries. 


chapter i 


Introduction 




3 


• Chapter 7 documents new recommended methodologies and procedures for 
using shallow-water monitoring data in assessing attainment of Chesapeake 
Bay water quality criteria and tidal water designated uses. 

• Chapter 8 documents a recommended 303(d) list decision-making framework 
for assessment of Chesapeake Bay and its tidal tributaries and embayments. 

This document represents the second formal addendum to the 2003 Chesapeake Bay 
water quality criteria document; as such, readers should regard the sections in this 
document as new or replacement chapters and appendices to the original published 
report. The criteria attainment assessment procedures published in this addendum 
replace and otherwise supercede similar criteria assessment procedures originally 
published in the 2003 Regional Criteria Guidance and 2004 addendum (U.S. EPA 
2003, 2004). Publication of future addendums by EPA on behalf of the Chesapeake 
Bay Program watershed jurisdictional partners is likely as continued scientific 
research and management applications reveal new insights and knowledge that 
should be incorporated into revisions of state water quality standards regulations in 
upcoming triennial reviews. 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for Chesapeake Bay and Its Tidal Tributaries. EPA 
903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for Chesapeake Bay and Its Tidal Tributaries - 
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, 
Annapolis. MD. 


chapter i 


Introduction 



4 


cha pter ii 


Refinements to the Chesapeake 
Bay Water Quality Criteria 
Assessment Methodology 


BACKGROUND 

The Chesapeake Bay water quality criteria were designed to protect the ecological 
integrity of the Bay’s tidal waters. To ensure that the criteria are being attained and 
the Chesapeake Bay ecosystem is, in fact, protected, adequate means to measure and 
evaluate water quality relative to the criteria must exist. The Bay is a highly diverse 
and variable system; these characteristics make precise assessment of water quality 
criteria attainment difficult. Thus, it is critical to design both a data collection system 
and a data analysis methodology carefully to make the best use of existing resources 
and provide the best possible assessment of water quality criteria attainment. Such a 
design can inform stakeholders about the status of impairments and whether the 
impairments have been removed once management actions have resulted in the 
achievement of the desired restoration goals. 

To address the need for enhanced water quality criteria assessments brought on by 
the states’ adoption of new Chesapeake Bay water quality standards, the Chesapeake 
Bay Program 1 redesigned its tidal monitoring network to provide a framework for 
interpreting the data. To the extent possible (within funding constraints), existing 
monitoring programs were either enhanced to support criteria assessment or new 
monitoring programs were established to address monitoring gaps. Given the diver¬ 
sity of tidal habitats throughout the Bay, establishing a comprehensive tidal 
monitoring network required different types of monitoring. 


'The Chesapeake Bay Program, formed in 1983 by the first Chesapeake Bay agreement, is a unique 
regional partnership guiding the restoration of the Chesapeake Bay and its tidal tributaries. On water 
quality issues, the Chesapeake Bay Program partners include Delaware, Maryland, New York. 
Pennsylvania, Virginia, West Virginia, the District of Columbia, the Chesapeake Bay Commission, the 
U.S. Environmental Protection Agency, over 20 other federal agencies, academic institutions, local gov¬ 
ernments, and citizen groups. 


chap-ter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 




5 


Developing a methodology for assessing criteria attainment using these data was 
also critical. Ideally the criteria assessment methodology would prove useful in 
several ways: I) it could be applied consistently for many w r ater quality criteria 
components; 2) it would provide a common framework for assessing data collected 
over multiple scales; 3) it would provide a basis for using as much of the informa¬ 
tion contained in the collected data as possible; 4) it would provide a clear basis for 
making decisions on criteria attainment; and 5) it would provide diagnostic infor¬ 
mation regarding the spatial and temporal patterns of criteria violations. The 
cumulative frequency diagram (CFD) approach, described in the original 2003 
Chesapeake Bay water quality criteria document, was designed with many of these 
objectives in mind (U.S. EPA 2003a). 


OVERVIEW OF THE CFD ASSESSMENT METHODOLOGY 

The original 2003 Chesapeake Bay water-quality criteria document fully describes 
the CFD methodology (Chapter 6, pages 154-178), but is summarized briefly here 
(U.S. EPA 2003a). Criteria assessment using the CFD methodology is based on 
interpolation within a spatially defined grid. Described later in this chapter, this grid- 
based interpolation provides the spatial framework for use of all of the data. It 
weights each data location according to the amount of area (or volume) it represents. 
Water quality parameter levels in all interpolator grid cells are estimated based on 
interpolation algorithms, providing a complete “map” of water quality throughout 
the assessed area (Figure II-1). Water quality parameter levels in each grid cell are 
compared to the applicable criteria levels to establish an estimate of the spatial extent 
of criteria exceedance (non-attainment). Aggregating the total amount of space (area 
or volume) in which the criteria are exceeded provides a basis for estimating the 
percentage of the spatial assessment unit (designated use within a segment) in which 
the criteria were exceeded for that monitoring cruise. These measures of criteria 
exceedance are then compiled over the entire assessment period to develop a cumu¬ 
lative frequency diagram, or CFD. The CFD is a well-known and well-established 
statistical procedure commonly used to describe hydrologic and environmental data 
(Helsel and Hirsch 1992). 

The CFD assessment methodology evolved from the need to allow for variability in 
water quality parameters due to unusual events. For the water quality parameter to 
be assessed, a criterion threshold is established; when the threshold is exceeded, the 
system is considered impaired. All water quality parameters, however, are inherently 
variable in space and time. Because of this variability, it is unlikely that even a 
healthy Chesapeake Bay ecosystem will attain the threshold absolutely in all places 
and at all times. 

Spatially, small regions may persistently exceed the criteria’s threshold due to poor 
flushing or other natural conditions. Such areas should not automatically lead to the 
assumption that the entire assessment unit is impaired. Similar logic applies in the 


chapter ii • 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 



6 



Figure 11-1. Example of interpolation of Chesapeake Bay water quality data. 


temporal dimension. Water quality in a large area of a segment may exceed the 
criteria’s threshold for a short time. If this degradation proves infrequent and short¬ 
lived with the segment quickly returning to a healthy state, this situation does not 
represent an impairment of the ecologically defined designated use of the segment. 

Recognition that ephemeral exceedances of the criterion’s threshold in time or space 
do not represent persistent impairment of the segment’s designated use ultimately 
led to the development of a criteria assessment methodology that deems such 
exceedances as acceptable. Persistent, widespread criteria exceedance, however, is 
considered an impairment of the segment’s designated use (U.S. EPA 2003a). 

The criteria assessment methodology determines how much of the spatial assess¬ 
ment unit is not in compliance with the criteria (percent of space) for each moment 
in time. In the second step of the methodology, a determination is made of how often 
(percent of time) a segment is out of attainment by more than a fixed percent of 


b 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 











7 


space. The results of these queries can be presented in graphical form with percent 
of time plotted against percent of space. 

Figure II-2 illustrates a typical CFD based on 12 measures of spatial extent of criteria 
exceedance over time. In general, if a segment is in attainment with the criterion, 
then one expects a high frequency of dates for which the percent out of attainment 
is low. In this case, the CFD should descend rapidly from the upper left comer, pass 
not far from the lower left corner, and then proceed to the lower right comer. The line 
in Figure II-2 shows the typical hyperbolic shape commonly observed using the CFD 
to assess water quality criteria in the Chesapeake Bay. The closer the CFD curve 
comes to the origin (lower left comer), the better the attainment of the assessed 
segment. A curve that is far from the origin indicates that a larger percent of space 
in the segment is out of attainment and the probability of use impairment increases. 

The CFD methodology offers many advantages over other criteria assessment 
approaches. Through interpolation, it provides a method for using data collected in 
areas surrounding the area of interest (the spatial assessment unit). This factor is 
important since the sample size of observations within a spatial assessment unit may 
not be sufficient to determine the area (or volume) of exceedance within the unit 
accurately. The method also weights the data collected from a given location 
according to the amount of area (or volume) that the location represents. This capa¬ 
bility is important because data may be collected from locations that do not represent 



Figure 11-2. A water quality criteria attainment assessment cumulative frequency diagram 
(CFD) based on 12 measures of the spatial extent of criteria exceedance over time. 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 









8 


all areas of the spatial assessment unit; providing equal weight to such data could 
bias the assessments. 

A second advantage is that the CFD incorporates the spatial-temporal pattern of 
criteria exceedance into the assessment. The shape of the curve offers information on 
patterns of exceedance in space and time. Such information may prove helpful in 
understanding the causes of impairments (see page 162 in U.S. EPA 2003a). 

A third advantage is that it bases the assessment on biologically determined patterns 
of allowable criteria exceedance. Reference curves are ideally developed in the same 
way as assessment curves and should reflect the degree of criteria exceedance that 
can be withstood by the ecological communities without impairing the designated 
use. Thus, comparison of the assessment curve to the reference curve ensures that 
any allowable criteria exceedances do not occur in a spatial or temporal pattern that 
could, in reality, represent impairment at the scale of the entire assessment unit (see 
pages 162-178 in U.S. EPA 2003a). Local persistent effects could still have high 
impairment. 

Finally, the combined elements of the CFD criteria assessment methodology fully 
and effectively address all five factors used to determine attainment of designated 
uses: magnitude, duration, frequency, space, and time. After conducting a national 
review of TMDL programs, the National Research Council (2001) concluded that 
establishing these conditions is crucial for successful application of state water 
quality standards. 

The CFD methodology is a new and innovative method of water quality criteria 
assessment, representing an improvement over methods used in other parts of the 
country (STAC 2006). The standard practice for assessing compliance with water 
quality criteria throughout the United States is by sampling monthly at a fixed set of 
stations and gauging compliance strictly from a count of exceedances of those 
samples. Sampling stations are typically located for convenience (e.g., accessibility). 
Consequently, reluctance to re-evaluate and change location (so as to maintain a time 
series at a fixed point) is common; no consideration is given to the representative¬ 
ness of the sample for the space/time not sampled. 

Most assessments are based simply on EPA assessment guidance in which all 
samples in a given area were compiled; attainment was assumed if no more than 10 
percent of the samples exceeded the standard (U.S. EPA 1997). In this approach, all 
samples are assumed to be fully representative of the specified space and time and 
are simply combined as if they were random samples from a uniform population. 
This approach was necessary in the past because the technology did not exist for a 
more rigorous method of data analysis; however, it neglected spatial and temporal 
patterns in the criteria measures. The CFD approach was designed to characterize 
these spatial and temporal patterns and weight samples more accurately based on the 
amount of space or time that they actually represent. 


chapter i 


Introduction 


9 


The CFD methodology was first applied in the Chesapeake Bay for the most recent 
303(d) listing cycle, completed in the spring of 2006 and based on data from 2002 
through 2004. The CFDs were developed and used primarily for the dissolved 
oxygen open-water and deep-water 30-day mean criteria because insufficient data 
and data analysis techniques existed to assess the higher-frequency dissolved oxygen 
criteria components. Similarly, the water clarity criteria were not assessed based on 
the CFD because few tidal systems had sufficient shallow-water monitoring data for 
an assessment. 

In fall 2005, the Chesapeake Bay Program’s Scientific and Technical Advisory 
Committee (STAC) established a scientific panel to review and refine the CFD 
assessment methodology. Nationally recognized academic experts in spatial and 
environmental statistics made up the panel. The STAC-convened panel concluded 
that the CFD approach is both feasible and innovative, qualifies as the best available 
science, and represents an improvement over criteria assessment methods used in the 
past (STAC 2006). 

The panel also recognized, however, that the approach remains in the early stages of 
management application. It stated that the CFD approach deserves further directed 
study and analysis to evaluate the bias and imprecision that can occur due to limita¬ 
tions in available data and in current interpolation and CFD algorithms (STAC 
2006). This chapter provides guidance for criteria assessment application, summa¬ 
rizes findings from the CFD evaluations, and offers recommendations for further 
refinement of the CFD assessment methodology. Appendix A provides a complete 
copy of the scientific panel’s final report. 


DESCRIPTION AND EVALUATION OF THE CFD-BASED 
ASSESSMENT METHODOLOGY 

The methodology for estimating the CFD is most easily described as a series of eight 
steps as shown in Table II-1. These steps, described below, provide a framework for 
considering the process and are elucidated by a simple example. More detailed 
discussions of each step follow later in this chapter. 

EXAMPLE CFD-BASED CRITERIA ASSESSMENT 

To illustrate the CFD criteria assessment methodology, a simple theoretical example 
based on a small data set can prove useful. Assume a segment for which the inter¬ 
polation grid is 4 cells by 4 cells. In reality, the number of grid cells is much larger 
(hundreds to thousands), but this small grid is illustrative. Also assume that data 
were collected on five distinct dates, and that each date is representative of the appro¬ 
priate time scale (in an actual application, data would be collected over many more 
dates). The criterion threshold for this fictitious water quality parameter is 3. 


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10 


Table 11-1. Steps for constructing and assessing criteria attainment using cumulative 
frequency diagrams (CFDs). 


1. Collect data from a spatial network of locations on several dates during the 
assessment period. 

2. For each date, interpolate the data spatially over the entire system to obtain esti¬ 
mates of water quality using a two- or three-dimensional grid of interpolation 
cells. 

3. Aggregate interpolations to the appropriate temporal scale (e.g., if evaluating the 
30-day mean, take the average of all interpolations for each date in the month). 

4. For each interpolator cell, assess whether the applicable criterion is exceeded. 

5. For each assessment unit, compute the percentage of interpolator cells that exceed 
the criterion as an estimate of the percent of area (or volume) within the spatial 
assessment unit that exceeds the criterion. 

6. Rank the percent of area estimates for the set of all sample days in the assessment 
period from largest to smallest and sequentially assign to these ranked percents a 
value that estimates percent of time. Add the end points of (100%, 0%) and (0%, 
100 %). 

7. Plot the paired percent of area (or volume) and percent of time data on a graph 
with the percent of area on the x-axis and percent of time on the y-axis. The 
resultant plot is the assessment cumulative frequency diagram or CFD. 

8. Compare the assessment CFD (from step 7) to the appropriate reference CFD. If at 
any point the assessment CFD exceeds the reference CFD (i.e., a given level of 
spatial noncompliance occurs more often than allowed for a given amount of 
time), then the criterion is in non-attainment. Consequently, the segment fails to 
meet that designated use. 


An illustration of the eight steps for computing the CFD for these simplified 
constraints is shown on the facing page. The three columns show the first three steps. 
Column 1 provides fictional data for five dates for five fixed locations in a two- 
dimensional grid. Column 2 shows a fictional interpolation of these data to cover the 
entire grid. Column 3 gives the compliance status of each cell in the grid with 1 indi¬ 
cating non-attainment and 0 signifying attainment. 

In this hypothetical example, the assessment curve is clearly greater than the refer¬ 
ence curve and in non-attainment of the criterion, therefore, the designated use is not 
met. EPA recommends that any exceedance of the attainment CFD above the refer¬ 
ence CFD should be considered non-attainment of the criterion and, consequently, 
the designated use. 


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11 


Step 1. Collect data at 
known locations. 


Date 


3 



3 



5 






2 



1 

Date 2 

1 



1 



3 






1 



1 

Date 3 

4 



2 



2 






1 



1 

Date 4 

1 



4 



2 






4 



1 

Date 5 

1 



3 



2 






1 



1 


Step 2. Interpolate the 
data to grid cells. 


Date 


3 

4 

5 

3 

4 

4 

5 

2 

3 

3 

4 

1 

2 

3 

3 

1 

Date 2 

1 

2 

3 

1 

2 

2 

3 

2 

1 

3 

2 

1 

1 

1 

1 

1 

Date 3 

4 

3 

2 

2 

3 

2 

2 

1 

2 

2 

1 

1 

1 

1 

1 

1 

Date 4 

1 

2 

3 

4 

2 

2 

2 

3 

3 

3 

2 

1 

4 

3 

1 

1 

Date 5 

1 

2 

3 

3 

2 

2 

2 

2 

1 

1 

1 

1 

1 

1 

1 

1 


Steps 3-4. Determine 
attainment status of 
each cell. 


Date 


1 

1 

1 

1 

1 

1 

1 

0 

1 

1 

1 

0 

0 

1 

1 

0 

Date 2 

0 

0 

1 

0 

0 

0 

1 

0 

0 

1 

0 

0 

0 

0 

0 

0 

Date 3 

1 

1 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

Date 4 

0 

0 

1 

1 

0 

0 

0 

1 

1 

1 

0 

0 

1 

1 

0 

0 

Date 5 

0 

0 

1 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 


Step 5: Determine 
percent attainment by date. 


Sample date 

Percent 

space 

Date 1 

75.00% 

Date 2 

18.75% 

Date 3 

18.75% 

Date 4 

43.75% 

Date 5 

12.50% 


Step 6. Rank the percent of 
space values and assign percent 
of time as (100*R/(N+1)), where 
R is rank and N is sample size. 


Sample date 

Ranked 

percent 

space 

Percent time 


100% 

0% 

Date 1 

75.00% 

16.67% 

Date 4 

43.75% 

33.33% 

Date 2 

18.75% 

50.00% 

Date 3 

18.75% 

66.67% 

Date 5 

12.50% 

83.33% 


0% 

100% 


Steps 7 and 8. Figure II-3 illustrates the plot of this 
theoretical assessment CFD and the comparison to a 
hypothetical reference curve. In this hypothetical ex¬ 
ample, the assessment area shows non-attainment. For a 
percent area of 18.75, the allowable frequency on the 
reference curve is about 17 percent. That is, 18.75 per¬ 
cent of the segment area should not be out of attainment 
more that 17 percent of the time. For Date 3, the esti¬ 
mated frequency of 18.75 percent of segment area in 
non-attainment is 66.67 percent. Thus the frequency of 
18.75 percent of space out of attainment exceeds the 17 
percent allowed. The reference curve is exceeded for 
dates 4 and 1 as well. 2 


2 ln this cumulative distribution framework, the actual date is 
not relevant. One should not infer that non-attainment occurred 
on that date if the data point associated with a date falls above 
the reference. The date is used here as a label for each coordi¬ 
nate pair. 


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12 



0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

Percent of Area Exceeding the Criterion 


Figure 11-3. Graphical representation of the CFD from the above theoretical example 
assessment curve (blue) with a hypothetical reference curve (black). 


CFD REFERENCE CURVES 

Two approaches are feasible in defining the reference curves proposed for use in the 
CFD assessment methodology. One is biologically based and identifies appropriate 
regions of the Bay, its tidal tributaries, and its embayments that have healthy biolog¬ 
ical indicators and are in attainment of their designated use (U.S. EPA 2003a). The 
CFDs are developed for these areas in the same way that assessment CFDs would be 
developed elsewhere. Curves generated for biologically healthy tidal areas are 
considered “reference” curves. 

For example, healthy benthic indices of biotic integrity (1BI) scores might be used as 
indicators of adequate bottom dissolved oxygen (Weisberg et al. 1997; U.S. EPA 
2003a). Thus, after stratifying by salinity zone and perhaps other factors, a series of 
dissolved oxygen reference CFD curves could be developed from the existing moni¬ 
toring database. One advantage of this approach is that each biological reference 
curve could be tailored to each designated-use-based criteria component. This tech¬ 
nique tailors the pattern of criteria exceedance that the part of the Bay ecosystem 
could tolerate and remain healthy to the protected species and biological communi¬ 
ties and the specific criterion component. Thus, each reference curve may have a 
somewhat different shape (see pages 168-177 in U.S. EPA 2003a). 


I 


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Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 



























13 


In some cases, development of a biologically-based reference curve is not possible 
due to lack of data describing the health of the relevant species or biological commu¬ 
nities. Such cases require a different approach. The EPA recommends use of a 
default reference curve in situations for which a biologically based reference curve 
remains unavailable. This default reference curve is defined as a hyperbolic curve 
that encompasses no more than 10 percent of the area of the CFD graph (percent of 
space multiplied by percent of time) (see page 174 in U.S. EPA 2003a) (Figure II-4). 
The default reference curve has the following important properties: 1) the plot is 
symmetric about the 1:1 line; 2) the plot is hyperbolic; 3) the total area under the 



Percent of Area/Volume Exceeding the Criterion 


Figure 11-4. Default reference curve for application in the attainment assessment of 
Chesapeake Bay water quality criteria for which biologically based reference curves 
cannot be derived. 


curve equals 10 percent; and 4) the ends of the curve pass through x- and y-axis 
intercepts (100, 0) and (0, 100), respectively. 

Figure II-4 is defined by the equation: 

(x + b)(y + b) = a Equation 1 

where: b = 0.0429945 and a = b : + b. 


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14 


No specific theoretical basis underlies this definition of the default reference curve, 
but the definition does provide equal weight to exceedances occurring in either space 
or time. This approach is appropriate since no information exists to indicate that 
either time or space should take precedence. Selection of the 10 percent value is 
based on its consistency with past national EPA guidance (U.S. EPA 1997). The 
default reference curve is hyperbolic, making it similar in shape to biologically 
based reference curves. In fact, the shape of the default reference curve is quite 
similar to some of the established biologically based reference curves, such as the 
30-day mean open-water dissolved oxygen reference curve (Figure 11-5). 



Figure 11-5. Biological reference curve for 30-day mean open-water dissolved oxygen 
criterion applied for assessment during the summer months (June-September) only. 


A default reference curve, defined as a hyperbolic curve encompassing no more than 
10 percent frequency exceedances, was also considered. Such a curve is based on a 
simple model: 

Xjj = u + aj + bj Equation 2 

where a is temporal term with variance <F 2 a and b is spatial term with d> 2 b . The vari¬ 
ance of x^ is <t> 2 . d + <I> 2 b = d> 2 . The standard deviation of x,j is sqrt(<5> 2 ) = <t>. Ten 
percent of the Xy should fall above u + 1.2815 * where 1.2815 is the 90th 
percentile of the standard normal distribution. Thus, assuming normality, a popula¬ 
tion with equal spatial and temporal variance and a mean that is 1.2815 * below 
the threshold criterion should have an exceedance rate of 10 percent over space and 
time. Figure II-6 shows the CFD for the 10 percent frequency exceedance default 
reference curve in black. 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 




















15 


Also plotted on this same axis in blue in Figure II-6 is a default reference curve 
based on 10 percent of the area of the percent space x percent time (the default refer¬ 
ence curve described previously and illustrated in Figure II-4). This evaluation was 
undertaken given an approach to deriving and assessing attainment of numerical 
chlorophyll a criteria is based largely on thresholds that should rarely be exceeded 
in healthy populations (e.g., the 90 th percentile). These two curves are very close in 
shape, further supporting the use of the default reference based on a 10 percent area 
under the curve. The EPA recommends use of the default reference curve, illustrated 
in Figure III-4 and defined by Equation 1, when an applicable biologically-based 
reference curve is not available. 



Figure 11-6 Comparison of hyperbolic curves based on 10 percent of area under the curve 
(blue) and 10 percent frequency exceedance (black). 


COMPARING ASSESSMENT AND REFERENCE CURVES 

Reference curves are more or less continuously defined while assessment curves 
have relatively few discrete measures. Biological reference curves can contain 
hundreds of points; the default reference curve has an infinite number of points. By 
contrast, curves for three-year assessments of summer (June-September) monthly 
means will have 12 data points with the curve defined by linear interpolation 
between neighboring points. For this reason, it is possible for portions of the assess¬ 
ment curve to be above the reference curve even without any measured point 
exceeding the reference curve. This situation becomes more comprehensible by 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 















16 


understanding that reference curves typically have positive curvature and that this 
curvature can dip below the line between consecutive points on the assessment 
curve, causing a spurious, non-allowable exceedance. 

To address this problem, the EPA recommends that reference curves be evaluated 
only at the temporal axis points in the assessment curve as illustrated in Figure II-7. 
For non-continuous biological reference curves, the points should be interpolated 
from neighboring points. Appendix B provides a detailed description of the complete 
Chesapeake Bay water quality criteria attainment assessment methodology. 



Oitsotvvd Oiygcn DW Monthly now cun* dttcrau point* CBfPM 700J—2004 


0-9 r 
0 . 8 ' 

0 . 7 ^ 


| 06 t 

j 4 

e o 4 4 

u. u - 4 ' r® 


0.3 

0.2 

0.1 

0.0 

0.0 






__ 


0.4 0.5 0.6 

Fraction ol Spaca 


Figure 11-7. The graph on the left (A) shows spurious non-attainment as the reference curve passes below the 
assessment curve between points. The graph on the right (B) shows attainment as the reference and assessment 
curves are evaluated at the same temporal axis points. 


DEVELOPMENT OF A STATISTICAL DECISION-MAKING FRAMEWORK 

A statistical framework for making decisions on water quality criteria attainment 
based on the CFD methodology would yield additional information on the certainty 
of the attainment decisions. It would also help direct appropriate monitoring strate¬ 
gies to reduce uncertainties. However, many theoretical obstacles remain in 
developing such a framework. The CFD methodology is a new and innovative 
approach to water quality criteria assessment. The relatively recent application of 
this methodology to criteria assessment suggests that conducting further evaluations 
and making improvements should prove constructive. The following section 
discusses the steps in applying the CFD methodology. 

Step 1—Data Collection 

One of the advantages of the CFD approach is that it can accommodate a variety of 
input data and still arrive at the same assessment endpoint. Data collection methods 
currently in place include: fixed-station data, cruise track data, continuous moni¬ 
toring data, aircraft flight path data, and satellite imagery data. Because of the 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 




















17 


interpolation step, all of these data can be used with varying degrees of success to 
estimate the total spatial distribution (to the limit of interpolator pixel size) of a water 
quality parameter. 

Step 2—Interpolation 

Interpolation can place data collected at various spatial densities on a common footing. 
On the one hand, this capability is advantageous because data collected at different 
spatial densities are available for the criteria assessment process. On the other hand, it 
can be misleading to accept interpolated surfaces from different data sources as equiv¬ 
alent without qualifying each interpolation with a measure of the estimation error 
associated with each data type. Clearly, an interpolation based on hundreds of points per 
segment (such as cruise track data) more accurately reflects the true non-attainment 
percentage when compared to an interpolation based on two or three points per segment 
(such as a fixed-station data). Of the various types of interpolation algorithms available 
and reviewed, kriging is best positioned to address this issue (STAC 2006). Kriging 
offers advantages over inverse distance weighting in that it provides the best assessment 
of the estimation error associated with interpolation, but has not been implemented to 
date. Other methods, such as interpolating polynomials, splines, and locally weighted 
regression methods, should also be explored. 

Step 3—Temporal Aggregation of Interpolations 

Depending on the interpolation method and the statistics available, it may be 
possible to calculate the probability of exceedance of the temporal mean at each 
point given the likely variance and the value(s) observed during the period. This step 
is necessary to calculate probabilities in the following step. 

Step 4—Pointwise Compliance 

Determining the percent attainment of each grid cell from each interpolation seems 
simple. If the estimated value for a grid cell is above (chlorophyll a) or below 
(dissolved oxygen, water clarity) the criterion, then that cell is not in attainment. 

While interpolation allows for standardization of many types of data, pointwise 
attainment determination allows for standardization of many criteria. Because attain¬ 
ment is determined at moments in time and points in space, it is possible to vary the 
criterion in time and space. If different levels of a water quality constituent are 
acceptable in different seasons, then the criterion can vary seasonally. It is possible 
to implement different criteria over space for a segment that bridges, for example, 
oligohaline and mesohaline salinity regimes. It might even be possible to let the 
criterion be a continuous function of some ancillary variable such as temperature or 
salinity, although this situation requires that such data exist for every interpolator 
cell. The only requirement is that the final attainment determination be “yes” or “no” 
for each interpolator cell. 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 


18 


Currently, limited pointwise attainment determination compliance has been im¬ 
plemented. For example, the open-water 30-day mean dissolved oxygen criterion is 
5 mgxliter' 1 , except when the ambient salinity drops below 0.5 psu and the criterion 
becomes 5.5 mgxliter' 1 (U.S. EPA 2003a). During the summer months, the open- 
water designated use boundaries are selected based on local density conditions 
reflecting stratification of the water column. 

Even the simplicity of this concept diminishes when examining interpolation error. 
Consider the assessment of two interpolator cells from an interpolation based on 
cruise track data. While both interpolations could have the same value, each could 
have a different level of error. Such different levels of error could mean that these 
were different probabilities that the criteria were actually exceeded. For the simple 
assessment of non-attainment, however, they count the same. Thus, one advantage of 
a statistical framework is that it accounts for different levels of error throughout the 
interpolation grid and these error levels could be incorporated into a single overall 
assessment of attainment. 

Step 5—Percent Non-Attainment in Space 

Computing a percentage should also be simple. The estimate is simply 100 times the 
number of cells not in attainment divided by the total number of cells. As a rule, the 
uncertainty of a binary process can be modeled using a binomial distribution. The 
issue of uncertainty described in step 3 propagates into computing the percent of 
attainment for a segment. In addition, estimated values for interpolator cells have a 
complex dependence structure, ruling out a simple binomial model. The rules 
governing the uncertainty of this step are also complex. The mathematics for 
modeling this propagation of error are feasible, but have not yet been developed. 

Step 6—Percent of Time 

While the CFD’s percent-of-space coordinate provides a simple interpretation of the 
percent of the spatial assessment unit that is out of attainment on a given date, the 
percent-of-time coordinate is not simply the percent of time out of attainment at a 
given point. Instead this coordinate is interpreted similarly to that of a cumulative 
distribution function; it represents the percent of time that the associated spatial 
percent of non-attainment is exceeded. For example, if the (percent space, percent 
time) coordinates for a point on the CFD are (90, 10), the spatial percent of non¬ 
attainment is greater than or equal to 90 percent about 10 percent of the time. 

This step is very similar to computing an empirical distribution function, which is an 
estimator of a cumulative distribution function. This similarity brings to mind 
statistical inference tools associated with empirical distribution functions—the 
Kolmogorov-Smimov, Shapiro-Wilk, Anderson-Darling, or Cramer-von Mises — as 
candidates for inference about the CFD (STAC 2006). These procedures model 
uncertainty as a function of sample size only (in this case, the number of sample 
dates). Since they do not account for uncertainty associated with the number of 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 


19 


samples collected in space (i.e., number of sampling stations), this indicates that they 
are to provide a statistical framework that truly accounts for error in Chesapeake Bay 
water quality criteria assessments. 

Steps 7 and 8—Plotting and Comparing the Curves 

When comparing the assessment curve to the reference curve, the issue of uncer¬ 
tainty becomes most important. The preceding discussion clearly indicates that 
uncertainty in the assessment curve represents an accumulation of uncertainty gener¬ 
ated in and propagated through the preceding steps. If the reference curve is 
biologically based, it is derived under the same system of error propagation. Devel¬ 
oping the statistical algorithms to quantify this uncertainty poses a challenge. 

Even if the uncertainty can be properly quantified, the issue of who gets the benefit 
of doubt due to this uncertainty can prove difficult to resolve. 

This problem of uncertainty in the regulatory process is widespread and not limited 
to the CFD approach. Nonetheless, it must be dealt with. One option is to require that 
the assessment curve be significantly above the reference curve to establish non¬ 
attainment. This option protects the regulated party from being deemed out of 
attainment due to random effects. If assessment CFD curves are not accurately deter¬ 
mined, however, it could lead to poor protection of environmental health and 
designated uses. A second option is to require that the assessment curve be signifi¬ 
cantly below the reference curve to establish attainment. This option results in strong 
protection of the environmental resource, but could lead to the regulated party imple¬ 
menting unnecessary and expensive management actions. 

Some compromise between these extremes is needed. The simplest compromise is 
to ignore variability and compare the assessment curve to the reference curve. As 
long as unbiased estimation is implemented for both the assessment curve and the 
reference curve, this third option will result in roughly equal numbers of false posi¬ 
tive (declaring non-attainment when, in fact, compliance exists) and false negative 
(declaring attainment when, in fact, non-attainment exists) results. This last 
approach is balanced and the one currently recommended by EPA. Under this 
approach, however, no mechanism exists to motivate error reduction by improving 
the data sets on which the criteria assessments are based. 

RESULTS OF THE SCIENTIFIC EVALUATION 

Beginning in fall 2005, the Chesapeake Bay Program’s Scientific and Technical 
Advisory Committee (STAC) appointed a panel of scientists to evaluate and refine 
the CFD water quality criteria assessment methodology. Evaluations included tests 
on the effects of: 1) sample densities in time and space; 2) varying levels of attain¬ 
ment; and 3) varying degrees of spatial and temporal covariance. Appendix A 
provides a complete copy of the panel’s final report while Appendix C offers a narra¬ 
tive evaluation of the options for spatial interpolation. 


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20 


In general, the STAC panel analysis and review indicated that the CFD approach can 
combine spatial and temporal data to support inferences on attainment or exceedance 
of the water quality criteria (STAC 2006). The panel viewed the CFD approach as 
innovative — one that has general application in water quality criteria assessments. 
In comparison to other jurisdictional authorities, the Chesapeake Bay Program has 
taken a lead in monitoring and assessment based upon scientific design (designated 
uses) and emphasis on statistical evidence. Advancement in the CFD approach 
should provide an important precedent for states outside the Chesapeake Bay region. 
Because the CFD is both feasible and innovative, the panel felt that it qualifies as the 
best available approach. On the other hand, the panel recognized that the approach 
remains nascent and deserves further directed study and analyses to evaluate the bias 
and imprecision that can occur due to small sample densities, non-independence in 
temporal trends, and inadequate spatial interpolations. 

The panel found that the CFD approach in its current form is feasible, but requires 
additional research to further refine and strengthen it as a statistical tool. The CFD 
builds on important statistical theory related to cumulative distribution functions; as 
such, its statistical properties can be simulated and deduced. In its analyses, the 
STAC panel showed that constructing confidence ellipses that support inferences 
related to threshold curves or other tests of spatial and temporal compliance are 
feasible. Understanding fundamental properties of how the CFD represents likely 
covariances of attainment in time and space and how temporal and spatial correla¬ 
tions interact with sample size effects require additional research. Further, 
researchers must also analyze biases across regions and designated-use segments. 
The panel expects that two to three years of directed research and development are 
necessary to identify and measure potential sources of bias and imprecision for 
criteria attainment determinations. 

In the near future, the panel foresees that the CFD approach will prove particularly 
powerful when linked to continuous spatial data streams through the cruise-track 
monitoring program, and when able to utilize continuous temporal data generated 
through further deployment of remote sensing platforms in the Chesapeake Bay 
(e.g., Chesapeake Bay Observing System). These data sets will allow greater preci¬ 
sion and accuracy in both threshold and attainment determinations made using the 
CFD approach. 

The STAC panel concluded that success of the CFD-based assessment rests upon 
decision rules related to the biological reference curves. These curves represent 
desired segment-designated use water quality outcomes and reflect sources of 
acceptable natural variability (STAC 2006). The reference and attainment curves 
should follow the same general approach in derivation: water quality data collection, 
spatial interpolation, comparison to biologically based water quality criteria, and 
combination of space-time attainment data through a CFD. Therefore, the biological 
reference curves allow implementation of a tolerance threshold presuming the data 
used to derive the reference curve were sampled similarly to the assessment curve. 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 



21 


That is, the reference curve defines the degree to which criteria violations can be 
tolerated without resulting in impairment of the designated use. 

Bias and uncertainty are driven in CFD curves by sample densities in time and space. 
Therefore, the STAC panel advised that similar sample densities be used in the deri¬ 
vation of assessment and reference curves. As such densities are not always feasible, 
additional analytical methods are needed to weight sampling densities equally 
between attainment and reference curves. 


APPLICATION OF THE CFD-BASED 
ASSESSMENT METHODOLOGY 

RECOMMENDATIONS FOR APPLICATION OF THE CFD-BASED 
METHODOLOGY 

As stated above, the CFD-based water quality criteria assessment methodology 
offers the potential for significant benefit in accurately assessing Chesapeake Bay 
water quality criteria attainment. As the STAC CFD Review Panel has indicated, 
however, that the methodology is new and additional evaluations and refinements 
should be performed (STAC 2006) (Appendix A). The EPA agrees with the panel’s 
conclusions, strongly supports the findings that further research is needed, and will 
support those efforts in whatever way possible in the coming years. In the meantime, 
the EPA recommends the following approach in undertaking Chesapeake Bay water 
quality criteria assessments. 

As described above, the Chesapeake Bay Program collects data at two different 
scales for water quality criteria attainment assessment. In each case, the design of 
data collection program focuses on assessments at a specific scale. The fixed-station 
data are designed for segment and baywide assessments and the shallow-water moni¬ 
toring data are designed to assess the small tidal tributaries and the Bay’s 
shallow-water habitats. Given the different scales, separate interpolations are likely 
necessary using the most appropriate interpolation algorithm. The STAC CFD 
Review Panel evaluated two possible options for spatial interpolation, recom¬ 
mending kriging as the better of the two alternatives (STAC 2006). Kriging, 
however, has not been fully developed for application in Chesapeake Bay water 
quality criteria attainment assessment. 

Until kriging is fully developed as an option for whole-Bay assessment based on the 
fixed-station data, the EPA recommends that spatial interpolations continue using the 
current Chesapeake Bay Program’s inverse distance weighting (IDW) algorithm- 
based interpolator (Appendix D). Spatial interpolation of the fixed-station data for 
assessment of criteria attainment in the mainstem Bay and major tidal tributaries 
requires several specific capabilities including: 1) the data must be interpolated in 
three-dimensions (i.e., with depth); 2) the data must be interpolated into the tidal 
tributaries and around bends in these tidal rivers; and 3) the interpolation needs to be 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 



22 


automated to complete large number of criteria assessments efficiently and routinely. 
These capabilities are not currently available using a kriging algorithm, but the 
Chesapeake Bay Program IDW interpolator is designed with these capabilities in 
mind. Thus, the EPA recommends that large-scale interpolations (segment, baywide) 
continue to be based on the fixed-stations data be performed using the Chesapeake 
Bay Program IDW interpolator. As kriging is developed further for use, this option 
may be recommended in the future. 

For the criteria assessment of small tidal tributaries and the Bay’s shallow-water 
habitats based on data from the shallow-water monitoring program, the EPA recom¬ 
mends implementation of a kriging algorithm, where possible. The shallow-water 
monitoring program yields data to assess criteria attainment in relatively few 
systems at any one time. Thus, it is possible to provide the more focused evaluations 
of individual interpolations that kriging requires. Furthermore, the intensive data 
collection provided by the shallow-water monitoring program is particularly 
conducive to detailed statistical analysis. To utilize the data’s information fully, a 
more thorough statistical interpolation procedure, such as kriging, should be imple¬ 
mented. The shallow-water systems are highly dynamic and thus better characterized 
by more intensive data collection combined with a more rigorous statistical interpo¬ 
lation algorithm. For these reasons, the EPA recommends that kriging be 
implemented, where possible, for criteria assessment based on shallow-water moni¬ 
toring data. 

Given the recommendation above, the EPA further advises that the states develop the 
expertise to perform spatial interpolation based on statistical methods. Assessment 
of the shallow waters will largely fall to the states, with some support from the 
Chesapeake Bay Program Office. Guidelines are being developed for the use of 
kriging in shallow-water criteria assessment. The procedure is detailed, however, and 
requires expertise in geographic information systems, spatial statistics, and computer 
programming. Questions remain about how best to implement kriging as an option 
for spatial interpolation. The EPA plans to provide support through the Chesapeake 
Bay Program Office to ensure that spatial interpolations based on kriging are 
performed consistently for all shallow waters of the Bay when practical. 

In general, most of the tidal waters of the Chesapeake Bay mainstem and major trib¬ 
utaries remain impaired. This judgment was confirmed by the assessments 
performed during the 2006 303(d) listing cycle and by listing decisions made prior 
to that time. The 2006 assessments indicated that many of the assessment units were 
far out of attainment with little need to confirm the conclusions through statistical 
analysis. As restoration efforts proceed and more Bay tidal waters approach attain¬ 
ment of their designated uses, then statistical procedures may become important to 
ensure that waters are properly removed from the 303(d) list as soon as possible. 
Given that it may require several years for the Bay to respond to management 
actions, there is ample time to conduct the studies necessary to develop the required 
statistical decision-making framework based on the CFD. The EPA recommends that 
assessment of criteria attainment continue as in 2006 when the decision rule was that 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 


23 


any criterion exceedance greater than that of the appropriate reference curve indi¬ 
cates non-attainment of that criterion and, therefore, the designated use. 

RECOMMENDATIONS FOR FUTURE REFINEMENT OF 
THE CFD-BASED ASSESSMENT METHODOLOGY 

As part of its conclusions, the STAC CFD Review Panel identified several critical 
remaining issues requiring resolution in the near future (STAC 2006). The EPA 
agrees with the recommendations for future development and advises that the Chesa¬ 
peake Bay Program partners ensure that the work is completed in a timely, 
appropriate manner. 

The following list identifies some of the critical aspects requiring further research as 
recommended by STAC (2006). See Appendix A for additional details. 

1. Effects of Sampling Density on CFD Results. The CFD is a special case of 
an unbiased estimator for a cumulative distribution function of a population. 
Like the cumulative distribution function, the CFD is a function of the mean 
and the variance of the population under assessment. The better the mean and 
variance are characterized with sample data, the more accurate the shape of the 
CFD. As the sampling density increases, the estimated CFD begins to approach 
the true CFD. If the sampling density is low, however, then sampling error 
could become important with the potential to affect the shape of the CFD and 
ultimately the accuracy of the compliance assessment. Furthermore, the poten¬ 
tial for sample size to affect the shape could create an attainment assessment 
bias if the reference curve and assessment curve are based on different 
sampling densities. Conditional simulation methods developed by the STAC 
panel show promise in resolving these issues and mitigating potential biases 
caused by sample size differences. 

2. Choice of Interpolation Method, The STAC panel’s research considered 
several interpolation methods and outlined the features of each (Table C-l in 
Appendix C). These features illustrated tradeoffs between ease of implementa¬ 
tion and maximizing information garnered from the data. Further work is 
needed to compare the features to the requirements of wide-scale implementa¬ 
tion of Chesapeake Bay criteria assessment procedures and to formulate a plan 
for tractable implementation that results in credible assessments. One strategy 
is to implement easily performed analysis (e.g., IDW) as a screening tool to 
identify cases for which attainment/non-attainment is clear, and then imple¬ 
ment more labor-intensive methods (e.g., kriging) for cases in which 
attainment is more difficult to resolve. 

3. Three-Dimensional Interpolation. Assessments of the dissolved oxygen 
criteria attainment requires three-dimensional interpolation. The field of three- 
dimensional interpolation, however, is not as highly developed as that of 
two-dimensional interpolation. Efforts are needed to evaluate research in three- 
dimensional interpolation further and to seek additional outside scientific input 


chapter ii 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 


and review to implement the best available technology for this aspect of criteria 
assessment. 

4. High-Density Temporal Data. As currently formulated, criteria assessment 
for most of the Bay’s open waters are based on “snapshots” in time of the 
spatial extent of criteria exceedance estimated through interpolation. Data 
collected for use in interpolation span several days given the large area being 
sampled. New technologies should soon be capable of producing high-density 
data in both space and time. Interpolation should accommodate data collected 
densely in space. It is unclear, however, how the CFD process will accommo¬ 
date data that are densely clustered in time. Further work is needed to evaluate 
methods to fully utilize the temporally intensive data currently being collected. 

5. Implementation and Review. As a rule of thumb, the best test of any new 
procedure is putting it to work with stakeholder involvement. Through its 
Criteria Assessment Protocols Workgroup, the Chesapeake Bay Program has 
already established a forum for resolving the details of CFD implementation. 
At appropriate intervals in this process, however, the Chesapeake Bay Program 
should seek independent scientific and technical review of the implementation 
status of the assessment methodology. 


LITERATURE CITED 

Helsel, D.R. and R.M. Hirsch.1992. Statistical Methods in Water Resources. Studies in Envi¬ 
ronmental Science #49. Elsevier Science Publishers, Amsterdam, Netherlands. 552 pp. 

Scientific and Technical Advisory Committee (STAC). 2006. The Cumulative Frequency 
Diagram Method for Determining Water Quality Attainment: Report of the Chesapeake Bay 
Program STAC Panel to Review of Chesapeake Bay Analytical Tools. STAC Publication 06- 
003. 9 October 2006. Chesapeake Bay Program Scientific and Technical Advisory 
Committee. Chesapeake Research Consortium, Edgewater, MD. 

U.S. Environmental Protection Agency (U.S. EPA). 1997. Guidelines for Preparation of the 
Comprehensive State Water Quality Assessments (305(b) reports) and Electronic Updates. 
Assessment and Watershed Protection Division, Office of Wetlands, Oceans and Watersheds, 
Office of Water, U.S. Environmental Protection Agency, Washington, D.C. 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office Annapolis, MD. 

Weisberg, S.B., J.A. Ranasinghe, D.M. Dauer, L.C. Schaffner, R.J. Diaz, and J.B. Frithsen. 
1997. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries 
20:149-158. 


Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 



25 


cha pter in 


Application of Chesapeake Bay 
Water Quality Criteria 
Assessment Procedures 


BACKGROUND 

Beginning in the late 1990s and continuing through 2003, the Chesapeake Bay 
Program partners developed new Chesapeake Bay water quality criteria designed 
specifically to protect the ecological health of the Bay (U.S. EPA 2003a). Delaware, 
Maryland, and Virginia, along with the District of Columbia, then adopted these 
criteria and new tidal water designated uses into their water quality standards regu¬ 
lations. The states’ new Chesapeake Bay water quality standards were applied for the 
first time in each state’s 2006 Clean Water Act 303(d) listing cycle. 

The four jurisdictions also adopted criteria assessment methods — published by EPA 
in 2003 and in a 2004 addendum — into state water quality standards regulations (U.S. 
EPA 2003a, 2004a). The methods characterize the spatial and temporal variability of 
the appropriate water quality parameters, while providing a clear basis for determining 
whether a portion of the Bay’s tidal waters reached attainment of the applicable desig¬ 
nated use. Despite the methods’ detail, technical limitations remained for their 
complete application. This chapter and those that follow address many of the prior 
technical limitations. Continued efforts to develop further refinements to the criteria 
assessment methodology in specific areas, however, will likely remain. 

In addition to the technical limitations, obstacles related to the states’ transition from 
an old set of water quality standards to the newer, more detailed Chesapeake Bay 
water quality standards also existed. Differences occurred in the spatial extent of past 
listing/delisting decisions. New water quality criteria components also exist that 
have never been previously assessed. Furthermore, the mechanisms and processes 
used to report listings in the past required updating to allow reporting based on the 
states’ new Chesapeake Bay water quality standards regulations. As with the tech¬ 
nical limitations referenced above, an ongoing effort to refine and update the 
methodology for making future listing decisions based on the new Chesapeake Bay 
water quality standards will also be required (see Chapter 8 for further details). 


chapter iii 


Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



26 


ASSESSMENT UNITS, SEGMENTATION, 

AND SUB-SEGMENTATION 

To assess attainment of the Chesapeake Bay water quality criteria, the spatial and 
temporal extent over which they apply must be defined. The temporal extent is 
defined implicitly for each component of the states’ Chesapeake Bay water quality 
standards. Described on page 150 in the 2003 EPA Chesapeake Bay water quality 
criteria document (U.S. EPA 2003a) and adopted into the jurisdictions’ water quality 
standards regulations, the spatial extent is defined by the intersection of a Chesa¬ 
peake Bay Program segment (U.S. EPA 2004b, 2005a) and each tidal water 
designated use (U.S. EPA 2003b, 2004c). The spatial units defined by this intersec¬ 
tion are referred to as “spatial assessment units.” The intent is for each unit to be 
assessed and listed independently on each jurisdiction’s 303(d) list (part 1 through 
part 5) (see Chapter 8 for further details). 

The scale of the Chesapeake Bay spatial assessment units is large, with selection 
based specifically on conditions in the Bay and on the factors affecting these condi¬ 
tions. The Chesapeake Bay Program segments themselves were based on salinity 
regimes, circulation patterns, and other natural physical features, but are generally 
reflective of variations in water quality conditions and living resource communities 
(U.S. EPA 2004b, 2005a). Thus, these segments serve as appropriate spatial units for 
measuring the scope of water quality impairments in the Chesapeake Bay, its tidal 
tributaries, and its embayments. They also work at a logical scale for developing 
necessary management plans (TMDLs). Many of the water quality impairments 
currently extend over large areas of the Bay and its tidal tributaries, so performing 
assessments and reporting on these impairments at the segment scale are both appro¬ 
priate. Developing management plans at this scale is also appropriate since multiple 
jurisdictions often contribute to impairments. 

Even though the scale of the spatial assessment units is suitable, in many cases it 
varied from the scale of past tidal water quality criteria attainment assessments. The 
change in scale introduced several challenges to the states as they implemented the 
new Chesapeake Bay water quality criteria and tidal water designated uses. Bound¬ 
aries of some previously established state assessment units were moved or shrunk to 
address the spatial variability in some state water quality standards assessment meas¬ 
ures. Furthermore, management decisions (e.g., listing certain waters as impaired, 
developing TMDLs) had already been made based on the previously established 
assessment units and were being implemented at the time the new Chesapeake Bay 
water quality standards were adopted into state regulation. Thus, it was necessary to 
establish procedures for transitioning to new spatial assessment units and relating 
prior management decisions to new assessments that were sometimes defined at a 
different spatial scale. 

In general, the states could address the differences in boundary locations by making 
small adjustments to state-defined spatial units. Primarily, adjustments consisted of 
small changes in the boundaries of the previously state-defined assessment units to 

chapter iii • Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



27 


make them coincident with the boundaries of the 
larger Chesapeake Bay Program segments. This 
way, the smaller assessment units nest within the 
larger ones and the larger-scale assessment results 
can be attributed to each of the smaller units within. 
The approach allows states to remain consistent 
with previous listing decisions while accounting for 
the broader designated-use-segment-assessment 
results on their 303(d) lists. 



In some cases, adoption of the new Chesapeake Bay 
spatial assessment units represented a less detailed 
and possibly less precise assessment of water-quality 
criteria attainment. For example. Figure III-1 illus¬ 
trates Chesapeake Bay Program segment CB7PH, 
which covers the southeastern portion of the main- 
stem Chesapeake within Virginia. As is typical in 
most of the Bay, the shoreline is extremely complex 
with many small tidal rivers, creeks, and embay- 
ments. These smaller tidal habitats may have different 
water quality than the mainstem Bay section of the 
segment due to different circulation patterns or land 
uses or pollution sources that dominate local water 
quality conditions. These smaller tidal habitats may 
even have monitoring information that demonstrates 
the differences in water quality conditions. In such a 
case, it may make sense to separate the smaller tidal 
river, creek, or embayment from the main assess¬ 
ment unit by subdividing it to create a new smaller 
spatial unit for separate assessment. Thus, the states ha 
larger units to characterize conditions in specific parts 
and embayments more precisely. 


Figure 111-1. Segment CB7PH covering the southeastern 
portion of the Chesapeake Bay in Virginia. 

Source: U.S. EPA 2004b. 

re the option to “sub-segment” 
of the Bay, its tidal tributaries, 


Allowing jurisdictions to subdivide the larger segments is consistent with national 
EPA guidance and with EPA-published Chesapeake Bay water quality criteria assess¬ 
ment guidance, which both provide specific considerations for sub-segmenting water 
bodies for criteria assessment and listing decisions (U.S. EPA 2003a, 2005b). 

Published EPA guidance states that waters can be partitioned “to represent homo¬ 
geneity in physical, biological or chemical conditions.” The EPA recommends that 
jurisdictions use similar principles in deciding to subdivide the larger Chesapeake 
Bay assessment units. A state’s decision to sub-segment an existing segment should 
be based on: 1) clear physical, biological, or chemical differences that can be docu¬ 
mented; 2) homogeneity of water quality in the water body under consideration; and 
3) confirmed future availability of monitoring data in the new sub-segment to provide 
the capability to assess conditions and allow a determination regarding its 303(d) list 
status. In all cases, there should be a priori knowledge of the conditions that support 
a decision to subdivide, and preferably specific data that demonstrate how conditions 

chapter iii • Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 













28 


differ in the area under scrutiny. Documentation of this information should be made 
available for review as part of the 303(d) listing cycle for which a new subdivided 
segment is initially assessed. Jurisdictions need to ensure that any sub-segmentation 
is fully consistent with their state’s water quality standard regulations. 

The EPA discourages states from subdividing segments simply to remove smaller 
areas from an impaired waters list. Given the tidal exchange that occurs among all 
segments, conditions in one segment can potentially affect adjacent segments. A sub- 
segment that is prematurely removed from the impaired waters list might require 
placement back on the impaired waters list in the next listing cycle due to adverse 
conditions in the original segment. 

Maryland and Virginia have already adopted specific sub-segments into their state’s 
water quality standards regulations in several tidal tributaries and embayments. The 
2004 addendum to the 2003 Chesapeake Bay use attainability and designated-use 
document contains detailed documentation supporting these state-defined, adopted 
sub-segmentations (U.S. EPA 2004c). 


DATA FOR USE IN CHESAPEAKE BAY 
CRITERIA ASSESSMENTS 

To assess Chesapeake Bay water quality criteria attainment, the data used must prove 
adequate. Consistent with the 2003 EPA Chesapeake Bay criteria assessment guid¬ 
ance, the data should be of known quality and adequate quantity, as well as 
representative of the tidal water designated use habitat under assessment (U.S. EPA 
2003a). Documented QA/QC programs should ensure data quality; such documen¬ 
tation should be publicly available for evaluation. A sufficient amount of data should 
exist to provide a defensible degree of accuracy and precision given the expected 
level of variability in the assessed tidal water body. The data should also be repre¬ 
sentative of the spatial assessment unit as a whole so the resulting assessment is not 
biased toward any one portion. While the EPA provides no minimum requirements 
for each of these data characteristics, they should be maximized to the extent 
possible to ensure that criteria assessments are scientifically defensible. 

Opinions range broadly on the quantity of data required for criteria assessment. On 
one extreme, some believe that sufficient data should be collected to capture all the 
temporal and spatial variability to ensure that the criteria and designated uses are 
attained in space and time. On the other extreme, some suggest that the state agency 
manager should determine if a designated use is being attained based on available 
information—even if it is anecdotal. 

For the Chesapeake Bay and its tidal tributaries, the EPA recommends basing all 
water quality criteria assessments on monitoring data. These data should be collected 
over a three-year period immediately prior to the year of the listing cycle, unless 
non-attainment is definitively established in less time (as described in Chapter 7). 
Furthermore, the monitoring program for data collection should optimize quality, 
quantity, and representativeness as described above. 

I 

chapter iii • Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



29 


The Chesapeake Bay Program partners continue to fund and conduct an extensive 
baywide, coordinated water quality monitoring program, much of which supports 
water quality criteria assessment. Water quality monitoring takes place at more than 
150 sites throughout the mainstem Chesapeake Bay and its tidal tributary waters 
(Figure III-2). Samples are collected at each of the fixed stations on a monthly or semi¬ 
monthly basis with data gathered since the mid-1980s (Chesapeake Bay Program 
1989). The fixed-station network provides consistent data over the entire mainstem 
Bay, major tidal tributaries, and larger embayments. The data are useful in assessing 
the published Bay water quality criteria in the open-water, deep-water, deep-channel, 
and migratory and spawning designated uses. 

Use of the fixed-station network is limited for criteria assessments in the shallow- 
water designated use habitats because the data scale is not appropriate. This network 
also proves limited in many smaller tidal tribu¬ 
taries and embayments, which have no or very few 
stations. To address these limitations, the Chesa¬ 
peake Bay Program partners developed a 
Shallow-water Monitoring Program to provide 
data collected intensively in space and time in the 
Bay’s shallow-water habitats. Chapter 7 describes 
this program and the details of data application for 
criteria assessment. 

The 2003 EPA Chesapeake Bay water quality 
criteria document describes the extent of data 
collection needed to assess the state’s Chesapeake 
Bay water quality standards (U.S. EPA 2003a). 

Three levels of effort are described for each crite¬ 
rion: marginal, adequate, and recommended (see 
pages 178-196 in U.S. EPA 2003a). The “mar¬ 
ginal” level of monitoring is the minimum data 
collection needed to support criteria assessment. 

At this level, data may not be of the right type or 
in sufficient quantity to assess all of the applicable 
criteria components. In general, this level of 
monitoring assumes that only the fixed-station 
data are available for criteria assessment. The 
“adequate” level of monitoring assumes that the 
fixed-station monitoring program will be 
combined with limited intensive data collection 
(e.g., temporally continuous monitoring for 
dissolved oxygen) to ensure that data are collected 
to support the assessment of all the applicable 
criteria components (e.g., 30-day, 7-day, and 1- 
day means, instantaneous minimum) in some 
spatial assessment units. The “recommended” 
level of monitoring assumes that the fixed-station 

chapter iii • Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



Figure 111-2. Locations of the sites that make up the 
fixed station network of the Chesapeake Bay Water 
Quality Monitoring Program. 

Source: Chesapeake Bay Program 1989. 



30 


monitoring program will be combined with intensive data collection in all spatial 
assessment units. Funding is not currently available to support monitoring at the 
“recommended” level. The fixed-station monitoring is expected to continue into the 
future, so data should be available at the “marginal” level for all spatial assessment 
units. With the implementation of the Chesapeake Bay shallow-water monitoring 
program in 2001, combined with a growing network of high-frequency observing 
system deployed in the Bay tidal waters, monitoring will reach the “adequate” level 
across all spatial assessment units with time. 

To enhance the monitoring information from the coordinated Chesapeake Bay water 
quality and shallow-water monitoring programs, jurisdictions are encouraged to 
include data from other sources as appropriate. Consistent with the 2003 EPA- 
published Chesapeake Bay water quality criteria assessment guidance, the states and 
the District are encouraged to compile data from sources such as state and federal 
monitoring agencies, local governments, universities, environmental organizations, 
and citizen monitoring groups (U.S. EPA 2003a). Such data could prove significant 
in enhancing the spatial coverage of the existing Chesapeake Bay water quality 
monitoring program. The jurisdictions must ensure, however, that the data are appro¬ 
priate for use in the Chesapeake Bay criteria attainment assessment methodology. 
Data need to be of documented quality and adequate quantity as indicated above. 

The jurisdictions also must ensure that the data are collected at an appropriate scale 
and are representative of a given area or volume of a specific spatial assessment unit. 
The Chesapeake Bay Program spatial interpolator uses data collected at all locations 
and defines how much of that area or volume can be characterized by data from a 
particular location (see Chapter 2 and Appendix D for details). Thus, a small tidal 
embayment may be characterized by data from a single site. If that site is not located 
properly (e.g., in a small creek, off a pier in shallow water, off a beach), the assess¬ 
ment of the entire embayment may rest on potentially biased information. Similarly, 
if data are collected intermittently at some sites, the spatial assessment unit may be 
characterized inconsistently at times. 

To use data collected through non-Chesapeake Bay Program monitoring programs in 
Chesapeake Bay water quality criteria assessments, they must be merged with the Chesa¬ 
peake Bay Program monitoring program data appropriately. The assumption is that these 
water quality data were collected on different time (more infrequent) and space (well 
away from the mid-channel river, mainstem) scales than the Chesapeake Bay Water 
Quality Monitoring Program data. Therefore, these other data will be assigned a cruise 
designation based on the monthly collection time so that they can be interpolated along 
with the Chesapeake Bay Water Quality Monitoring Program data to generate the cumu¬ 
lative frequency distribution (see Chapter 2 and Appendix B for details). 

The states are encouraged to seek data from sources beyond the Chesapeake Bay 
Water Quality Monitoring Program, but should use such data with care to avoid 
biasing the assessment results for any particular portion of the tidal waters. Ideally, 
the states would work with the collecting agencies and institutions in advance to 


chapter iii 


Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 


31 


ensure that the data are collected appropriately for use in interpolation and the 
overall CFD-based criteria assessment methodology. 

In addition to data collected by government and non-profit agencies, the states are 
also encouraged to work with agencies, organizations, or other entities subject to 
regulation, but with an interest in contributing data for use in the criteria attainment 
assessment process. Such agencies may be able to provide additional monitoring 
resources and significant amounts of supplementary data. Provided that an adequate 
QA/QC program is in place to ensure that the data are accurate, representative, and 
of known quality, these regulated agencies or entities may significantly benefit the 
criteria assessment process. 

The Hampton Roads Sanitation District in Virginia is one such example. The District 
has worked with the Virginia Department of Environmental Quality and the Virginia 
Institute of Marine Science to establish its own shallow-water monitoring program. 
The Virginia Department of Environmental Quality can use the data generated by the 
program to assess the state’s dissolved oxygen, water clarity, and chlorophyll a 
criteria in the lower tidal James River. Other similar organizations of regulated stake¬ 
holders may also wish to provide similar data. 


UPDATING THE CRITERIA ASSESSMENT FRAMEWORK 

The criteria assessment methodology developed for the Chesapeake Bay water 
quality criteria standards will require continued refinement into the future. The tech¬ 
nical details of the methodology continue to be refined through research and 
experience with application. This document describes many new refinements that 
will assist the jurisdictions with their criteria assessment process and listing deci¬ 
sions. More refinements are expected over the coming years. Furthermore, better 
understanding is developing with time as more data are collected. New monitoring 
programs (e.g., shallow-water monitoring) are offering new insight into the 
processes that affect water quality conditions in the Chesapeake Bay. This enhanced 
understanding will help fine-tune the requirements necessary for protection of the 
Bay ecosystem. Given that continued refinements of the criteria assessment method¬ 
ology are expected, it is recommended that the jurisdictions plan continued updates 
to their Chesapeake Bay water quality standards regulations through their existing 
triennial review process. The EPA commits to providing the information needed for 
updating the states' water quality standards through publication of recommended 
refinements to the criteria assessment procedures (such as in this addendum). The 
publication of any future addendums to the 2003 Chesapeake Bay criteria document 
will come in advance of the jurisdictions’ triennial reviews for use in justifying 
needed changes to the state’s water quality standards regulations. 

One example of the expected refinements to the criteria assessment methodology is 
the development of a statistical basis for decision-making using the CFD (see 
Chapter 2 and Appendices A and C for further details). Since the Chesapeake Bay 
criteria assessment methodology was first published in 2003, interest has grown in 


chapter iii 


Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



32 


developing an accounting of error in the assessment process. Research has been 
underway over the past years to develop such a methodology. The technical details 
are challenging, however, and research has not yet led to a solution. Progress has 
occurred over the last year; a statistical framework could possibly be developed for 
adoption into the state’s water quality standards in upcoming 303(d) listing cycles. 
Other refinements may be developed for monitoring programs and the interpolation 
procedures. The EPA encourages states to prepare for adopting such refinements to 
their criteria assessment procedures into future regulations. 

Reference curves provide a second example of expected refinements. As more data 
are collected, the capability for better defining the amount and pattern of criteria 
exceedance that the system can withstand will continually improve. While major 
changes to the reference curves are not expected, updating the reference curves with 
additional data will improve the states’ ability to assess Chesapeake tidal waters 
accurately. With the prior agreement of the watershed jurisdictions, the EPA will 
update the reference curves with new data and publish the revised curves in future 
criteria document addenda. The jurisdictions will then need to adopt the new refer¬ 
ence curves into their water quality standards regulations through their regular 
triennial review processes. 


LITERATURE CITED 

Chesapeake Bay Program. 1989. Chesapeake Bay Monitoring Program Atlas-Volume I: 
Water Quality and Other Physiochemical Monitoring Programs. CBP/TRS 34/89. U.S. Envi¬ 
ronmental Protection Agency Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency (U.S. EPA). 2003a. Ambient Water Quality’ Criteria for 
Dissolved Oxy’gen, Water Clarity> and Chlorophyll a for the Chesapeake Bay and Its Tidal Trib¬ 
utaries. EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Ambient Water Quality’ Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a Chesapeake Bay and its Tidal Tributaries-2004 
Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. EPA 903-R-04-008. 
CBP/TRS 268/04. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004c. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability-2004 Addendum. EPA 903-R-04-006. Region III 
Chesapeake Bay Program Office Annapolis, Maryland. 

U.S. Environmental Protection Agency. 2005a. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. 2005 Addendum. 
EPA 903-R-05-004. CBP/TRS 278-06. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2005b. Guidance for 2006 Assessment, Listing and 
Reporting Requirements Pursuant to Sections 303(d) and 314 of the Clean Water Act. 
Watershed Branch, Assessment and Watershed Protection Division. Office of Wetlands, 
Oceans and Watersheds, Office of Water, U.S. EPA, Washington, D.C. 


chapter iii 


Application of Chesapeake Bay Water Quality Criteria Assessment Procedures 



33 


chapter 8\/ 


Refinements to the Chesapeake 
Bay Dissolved Oxygen Criteria 
Assessment Procedures 


BACKGROUND 

In 2003, the EPA published detailed criteria for dissolved oxygen tailored to 
different habitats within the Chesapeake Bay and its tidal tributaries (U.S. EPA 
2003a) (Table IV-1). Oxygen is critical to most forms of life in the Bay; it must be 
available in adequate concentrations to support overall ecosystem health. Minimum 
concentrations of oxygen must be present to support the wide range of species 
requiring protection as well as their various life stages. 

Dissolved oxygen criteria were established for Chesapeake Bay that varied in space 
and time to provide levels of protection for different key species and communities. 
The criteria were also designed around several lengths of time to reflect the varying 
oxygen tolerances for different life stages (e.g., larval, juvenile, adult) and effects 
(e.g., mortality, growth, behavior). Thus, the dissolved oxygen criteria include 
multiple components. Each component includes a target of dissolved oxygen 
concentration, the duration of time over which the concentration is averaged, the 
space (designated-use area) where the criterion applies, and a time (season, month) 
when the criterion applies. 

The dissolved oxygen criteria include 30-day, 7-day, and 1-day means along with an 
instantaneous minimum. Each of these criteria components applies to a specific 
season, such as the migratory spawning nursery period or the summer months (June 
through September) or all-year round. Each also relates to one of four tidal-water 
designated uses, according to the species and biological communities to be protected 
(U.S. EPA 2003a, 2003c). The EPA published, and the states adopted into their water 
quality standards regulations, dissolved oxygen criteria protective of migratory and 
spawning, open-water, deep-water, and deep-channel designated-use habitats (U.S. 
EPA 2003a) (Table IV-1). 


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Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



34 


Table IV-1. Chesapeake Bay dissolved oxygen criteria. 


Designated 

Use 

Criteria 

Concentration/Duration 

Protection Provided 

Temporal 

Application 

Migratory 

fish 

spawning 

and 

nursery use 

7-day mean > 6 mg liter 1 
(tidal habitats with 0 -0.5 ppt 
salinity) 

Survival/growth of larval/juvenile tidal- 
fresh resident fish; protective of 
threatened/endangered species 

February 1 

May 31 

Instantaneous minimum > 5 
mg liter' 

Survival and growth of larval/juvenile 
migratory fish; protective of 
threatened/endangered species 

Open-water fish and shellfish designated-use criteria apply 

June 1 - 
January 31 

Shallow- 
water Bay 
grass use 

Open-water fish and shellfish designated-use criteria apply 

Year-round 

Open-water 
fish and 
shellfish use 

30-day mean > 5.5 mg liter 1 
(tidal habitats with 0-0.5 ppt 
salinity) 

Growth of tidal-fresh juvenile and adult 
fish; protective of threatened/ 
endangered species 

Year-round 

30-day mean > 5 mg liter' 1 
(tidal habitats with > 0.5 ppt 
salinity) 

Growth of larval, juvenile and adult fish 
and shellfish; protective of threatened/ 
endangered species 

7-day mean > 4 mg liter 1 

Survival of open-water fish larvae 

Instantaneous minimum > 3.2 
mgliter' 1 

Survival of threatened/endangered 
sturgeon species 1 

Deep-water 
seasonal fish 
and shellfish 

use 

30-day mean > 3 mg liter' 1 

Survival and recruitment of bay 
anchovy eggs and larvae 

June 1 - 
September 30 

1-day mean > 2.3 mg-liter ' 

Survival of open-water juvenile and 
adult fish 

Instantaneous minimum > 1.7 
mgliter' 1 

Survival of bay anchovy eggs and larvae 

Open-water fish and shellfish designated-use criteria apply 

October 1 - 
May 31 

Deep- 
channel 
seasonal 
refuge use 

Instantaneous minimum > 1 
mgliter 1 

Survival of bottom-dwelling worms and 
clams 

June 1 

September 30 

Open-water fish and shellfish designated-use criteria apply 

October 1 

May 31 


1 At temperatures considered stressful to shortnose sturgeon (> 29EC), dissolved oxygen concentrations above an 
instantaneous minimum of 4.3 mgliter 1 will protect survival of this listed sturgeon species. 


chapter iv • 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 


























35 


Assessing dissolved oxygen criteria attainment is challenging because of the 
complexity of both the criteria and the Bay itself. To fully assess all the criteria 
components, data need to be collected at a spatial intensity that adequately represents 
the four designated-use habitats of Chesapeake Bay tidal waters at different times of 
the year (U.S. EPA 2003c, 2004b). Similarly, data must be collected during all the 
applicable seasons and at frequencies sufficient to address the various criteria dura¬ 
tion components. The different dissolved oxygen criteria apply to different 
designated-use areas and multiple criteria apply to the same designated-use area. The 
dissolved oxygen criteria components also apply over different time periods to 
protect species during critical life stages or during particularly stressful times of the 
year. To fully assess each dissolved oxygen component in each designated-use 
habitat over the appropriate time periods will require an extensive monitoring 
program and a detailed assessment methodology. The Chesapeake Bay Program 
currently conducts extensive water quality monitoring throughout the Bay tidal 
waters and the EPA published a detailed dissolved oxygen criteria assessment 
methodology with the new water quality criteria (Chesapeake Bay Program 1989; 

U.S. EPA 2003a, 2004a). The existing Bay water quality monitoring was not suffi¬ 
cient to cover all the criteria components, however, and some details in the 
assessment methodology remain unresolved. 

For the 2006 303(d) listing cycle, the states’ 
listing decisions were based primarily on 
previous listings. Tidal waters that had been 
listed as impaired in 2004 were not removed 
from part 5 of their listing unless all the appli¬ 
cable criteria components were shown in 
attainment (see Chapter 8 for further details). 

The Chesapeake Bay Program partners had the 
capacity (data, assessment methodology) to 
assess only the 30-day mean dissolved oxygen 
criteria and, in some cases, the instantaneous 
minimum dissolved oxygen criteria. The 
remaining dissolved oxygen criteria were not 
assessed because the existing water quality 
monitoring programs and the published assess¬ 
ment methodologies were inadequate for full 
assessment. In most spatial assessment units, 
the 30-day mean criterion was not attained and 
those assessment units would have been listed 
whether or not the other applicable dissolved 
oxygen criteria were also assessed (Figure IV- 
1). In many smaller tidal tributaries, however, 
the 30-day mean criterion was attained and 

those spatial assessment units were listed either Figure IV-1. Listing status of the Chesapeake Bay open-water 

l4 . . ,,, . „ c . , ... designated use based on dissolved oxygen standards, 

as impaired (part 5) due to previous listing or 



chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 




36 


as having “insufficient data to assess” (part 3). As nutrient loads are reduced and Bay 
water quality improves, assessing the complete array of applicable dissolved oxygen 
criteria to remove spatial assessment units from the “impaired” list will become 
more critical. 

Since Chesapeake Bay dissolved oxygen criteria were published in 2003, the capa¬ 
bility of fully assessing all the dissolved oxygen criteria for all four designated uses 
over all applicable time periods has progressed, but some limitations remain. The 
refined and expanded dissolved oxygen criteria assessment methodologies docu¬ 
mented in this chapter replace the methodologies previously published by U.S. EPA 
(2003a, 2004a). Work should continue in refining these methodologies to reduce 
uncertainty further and to increase confidence in the resulting assessments. Devel¬ 
oping, validating, and publishing EPA-recommended methodologies for assessing 
the full array of Chesapeake Bay dissolved oxygen criteria duration components will 
also prove critical. 


TEMPORAL PERIODS FOR ASSESSMENT 
OF DISSOLVED OXYGEN CRITERIA 

To assess dissolved oxygen criteria attainment, the time span over which the criteria 
apply must be clearly defined. In some cases, the temporal period is defined implic¬ 
itly as part of the criteria. For example, the dissolved oxygen criteria protective of 
the migratory fish spawning and nursery habitat designated use apply only to that 
time of year when spawning fish (and the resultant eggs and early juveniles) require 
higher dissolved oxygen levels compared to the rest of the year. In this example, 
dissolved oxygen criteria attainment should be assessed over the entire spawning 
season (February 1 through May 31) (U.S. EPA 2003a). Similarly, dissolved oxygen 
criteria in the deep-water and deep-channel designated uses apply only during the 
summer months — June 1 through September 30 — when the Bay stratifies and 
naturally reinforces the potential for lower dissolved oxygen concentrations in 
deeper waters. Therefore, assessment of dissolved oxygen criteria attainment in the 
deep-water and deep-channel designated uses should also be performed over the 
entire 4-month summer season (U.S. EPA 2003a). In all these cases, data are 
collected over the entire criteria season in each of the three years of the assessment 
period and these data are used to develop the cumulative frequency diagram (CFD) 
for assessing dissolved oxygen criteria attainment (see Chapter 2 and Appendix B for 
additional details). 

Open water is the only tidal water designated use in which the dissolved oxygen 
criteria apply year-round (U.S. EPA 2003a). In general, the Bay is most vulnerable to 
low dissolved oxygen during the summer when temperatures are high, oxygen solu¬ 
bility is low, and biological consumption of oxygen rises to its greatest level. Periods 
of low dissolved oxygen can also occur during the rest of the year, sometimes caused 
by high loading with subsequent slow consumption of organic material. The open- 


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Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



37 


water dissolved oxygen criteria are designed to provide protection of open-water 
habitat fish and shellfish communities at all times of the year. In spite of the year- 
round application of these criteria, natural processes complicate the use of a single, 
year-round assessment. Cooler temperatures affect the solubility of oxygen and allow 
higher concentrations compared to similar organic loading conditions in warmer 
months. Consequently, dissolved oxygen concentrations have a large natural vari¬ 
ability range. Detecting human effects in the presence of that greater variability often 
proves difficult. For this reason, as part of the dissolved oxygen criteria development 
process, the EPA originally intended that the year-round open-water dissolved oxygen 
criteria (see Table III-10, page 66 in U.S. EPA 2003a) be assessed in each season (see 
pages 150-151 in U.S. EPA 2003a). During the 2006 303(d) listing cycle, confusion 
arose as to the appropriate time period for open-water dissolved oxygen assessment. 
The criteria were clearly defined over the full annual cycle, but the stated intent was 
to assess them on a seasonal basis. Furthermore, the 2003 EPA Chesapeake Bay 
criteria document itself did not provide consistent guidance; it referred to assessment 
on both an annual basis and a seasonal one (U.S. EPA 2003a). 

Based on a re-evaluation of the underlying scientific basis for Chesapeake Bay 
dissolved oxygen criteria, the EPA recommends that jurisdictions assess attainment 
of the open-water dissolved oxygen criteria separately over two time periods: 
summer (June 1 through September 30) and non-summer (January 1 through May 31 
and October 1 through December 31). The open-water dissolved oxygen criteria 
were largely derived to protect open-water species during the summer when elevated 
temperatures, higher salinities, and naturally low dissolved oxygen levels occur 
(U.S. EPA 2003a). Given that summer is a critical period for many species, it should 
be assessed separately. The potential for dissolved oxygen impairments are lower in 
the non-summer period due to greater natural dissolved oxygen solubility and lower 
biological oxygen consumption—both due to lower water column temperatures. 
Nevertheless, low dissolved oxygen levels sometimes occur during other times of the 
year making a separate dissolved oxygen criteria assessment necessary for the non¬ 
summer period. The separate criteria assessments for summer and non-summer 
seasons will support year-round protective dissolved oxygen concentrations in the 
open-water designated-use habitats. 


DISSOLVED OXYGEN CRITERIA ASSESSMENTS 
IN SHALLOW VERSUS OPEN WATERS 

The open-water designated-use boundary is explicitly defined as including “tidally 
influenced waters extending horizontally from the shoreline to the adjacent shore¬ 
line” (see page 71 in U.S. EPA 2003c). Further, on page 68, the U.S. EPA (2003c) 
states that: 

The shallow-water bay grass designated use is intended specifically to delin¬ 
eate the habitats where the water clarity criteria would apply. The 


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Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



38 


open-water fish and shellfish designated use and the accompanying dis¬ 
solved oxygen criteria will fully protect the biological communities 
inhabiting shallow-water habitats. The open-water designated use extends 
into the intertidal zone and protects shallow-water organisms beyond under¬ 
water bay grasses. 

Unless a state has specifically delineated a sub-segment within a segment, attainment 
of the open-water designated use will be based on dissolved oxygen criteria attain¬ 
ment for the entire volume of the open-water designated use within the segment. 
Neither the need nor the requirement exists for a separate assessment of dissolved 
oxygen criteria attainment strictly within shallow waters (0-2 meters in depth). The 
importance of acquiring better temporal and spatial coverage of dissolved oxygen 
conditions in these shallow-depth habitats is not diminished however, since condi¬ 
tions in these areas vary greatly from the open water of the mid channels where the 
fixed stations are located. Shallow-water monitoring will provide the data needed to 
characterize dissolved oxygen conditions in shallow-water habitats more fully (see 
Chapter 7 for further details). 


ASSESSMENT OF SHORT-DURATION 
DISSOLVED OXYGEN CRITERIA 

Historically, the Chesapeake Bay Water Quality Monitoring Program consisted 
primarily of fixed-station monitoring conducted on a monthly or twice-monthly 
basis (Chesapeake Bay Program 1989). This sampling design was primarily intended 
to assess long-term trends in water quality and the status of living resources, 
capturing variability over decadal, annual, and seasonal time scales. The fixed- 
station monitoring was adapted to assess the 30-day mean dissolved oxygen criteria 
to measure dissolved oxygen throughout the Bay and its tidal tributaries and embay- 
ments. This system ensures at least one set of measurements for each month. 

The individual monthly estimates are considered accurate, although imprecise, since 
the sample sizes are small (n = 1 or 2). This imprecision is likely to be mitigated by 
the many estimates of monthly means (e.g., multiple months over the 3-year assess¬ 
ment period), which are combined into each single assessment of criteria attainment 
(see Chapter 2 and Appendix B for additional details). The monthly and twice- 
monthly fixed-station data are not adequate to assess attainment of the 7-day and 
1-day mean dissolved oxygen criteria directly because the sampling frequency rests 
outside the defined time intervals and is unable to capture the short-term variability. 

For the 2006 303(d) listing cycle, only three of the dissolved oxygen criteria compo¬ 
nents were assessed. The 30-day mean open-water criterion was determined in all of 
the assessment units of Chesapeake Bay using the fixed-station data and the CFD 
assessment methodology. In spatial assessment units where deep-water and/or deep- 
channel designated uses exist, the 30-day mean deep-water criterion and the 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 




39 


instantaneous minimum deep-channel criterion were also determined using the 
fixed-station data. 

The rationale behind the assessment of the instantaneous minimum deep-channel 
criterion was based on the long-term fixed station data record in the deep-channel 
locations which shows that dissolved oxygen does not vary strongly through time in 
the deep channel during the summer months because of the physical isolation from 
the atmosphere and the photic zone. Dissolved oxygen concentrations remain rela¬ 
tively constant; therefore, a 30-day mean should be similar to any instantaneous 
measure (see section below). 

No assessments were made of the 7-day and 1-day mean dissolved oxygen criteria 
because the data were considered inadequate (as described above). In most cases, 
this situation did not affect listing decisions because many spatial assessment units 
did not attain the 30-day mean criterion (see Figure IV-1) and all criteria components 
need to be attained to justify removal from the impaired list (part 5). The 30-day 
mean criterion was attained in some cases. These spatial assessment units, if not 
previously listed on part 5. were placed in part 3 of the states’ lists for waters with 
insufficient data (see Chapter 8 for further details). As water quality conditions 
improve in Chesapeake Bay, a method to assess higher frequency dissolved oxygen 
criteria will be needed so that spatial assessment units in attainment with all appli¬ 
cable dissolved oxygen criteria components can be removed from the state’s 
impaired waters list (see Appendix E). 

Until the EPA publishes methodologies for assessing the 7-day mean, 1-day mean 
and instantaneous minimum open-water and deep-water dissolved oxygen criteria 
components, the agency recommends that the states rely strictly on the assessment 
of the 30-day mean open-water and deep-water dissolved oxygen criteria for listing 
decisions. For those open-water and deep-water designated-use segments in which 
the 30-day mean criteria are not in attainment, the jurisdictions should list the desig- 
nated-use-segment on part 5 as impaired in the absence of data and/or methodologies 
for assessing the remaining criteria components. For those designated-use segments 
in which the 30-day mean criteria are in attainment, the jurisdictions should generate 
additional data and apply the criteria assessment procedures to assess attainment of 
the 7-day mean, 1-day mean, and instantaneous minimum criteria components. 


DISSOLVED OXYGEN REFERENCE CURVES 

SUMMER OPEN-WATER AND DEEP-WATER DISSOLVED OXYGEN 
CRITERIA REFERENCE CURVES 

Reference curves for both the 30-day mean open-water (June 1-September 30 only) 
and 30-day mean deep-water dissolved oxygen criteria were based on criteria levels 
that would not impair biological communities (U.S. EPA 2003a). Reference areas for 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



40 


derivation of the 2003 published deep-water reference curves were identified using 
a measure of benthic community health—the Chesapeake Bay benthic index of 
biological integrity or benthic-IBl (Weisberg et al. 1997). Sessile benthic communi¬ 
ties are good indicators of the water quality of the overlying waters. Although 
relatively tolerant of lower oxygen concentrations, a dissolved oxygen concentration 
of 2 mg liter' 1 is the threshold below which benthic infaunal communities become 
severely stressed (numerous references cited in Chapter 3 of U.S. EPA 2003a). A 
healthy benthic community, therefore, could indicate allowable time and space 
exceedances of the dissolved oxygen criteria that will not impair the biological 
community. 

Benthic infaunal community samples are collected as part of the long-term Chesa¬ 
peake Bay Benthic Monitoring Program at fixed and random locations during the 
summer, usually in August to September. If the benthic-IBl of that sample is “good,” 
(in this case 3 or more on a scale of 1 to 5), dissolved oxygen conditions were likely 
adequate for the previous one to two months (Dauer et al. 2005). 

In order to ensure greater consistency in deriving the open-water and deep-water 
reference curves, factor in the state-adopted designated-use boundaries and take 
advantage of a full two decades on monitoring data, both reference curves were 
updated. To develop updated open-water and deep-water reference curves, the 
monthly fixed and random station locations for the benthic-IBI data from 1985 to 
2005 were matched with the monthly open-water and deep-water designated-use 
boundaries for the same time period. This updated approach differs from the original 
method published by the EPA (2003a), which used a single designated-use boundary 
coverage for the entire data record. An additional difference is that previously this 
method was used to define only the deep-water reference curve. The open-water 
reference curve was based on an analysis in which “good” water quality conditions 
were defined for reference segments by year (see Appendix H in U.S. EPA 2003a). 

Reference locations were identified by sorting the resulting data set by year, 
segment, and designated use. If a designated use in a given segment in a given year 
had only “good” benthic-IBI scores (>3), then the dissolved oxygen data for that 
segment, designated use, and summer period (June-September) can be used to 
compute a reference curve. Appendix F lists these use-segment-year combinations. 
Separate CFDs were generated for open-water and deep-water designated-use habi¬ 
tats from the entire data set of summer dissolved oxygen data from all reference 
locations over the 1985-2005 data record. Figures IV-2 and IV-3 respectively illus¬ 
trate the resultant June-September open-water and deep-water dissolved oxygen 
criteria reference curves. Appendix G documents the equations for the reference 
curves. 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



41 


Open Water Monthly Dissolved Oxygen Biological Reference Curve 



Percent of Volume 


Figure IV-2. Chesapeake Bay open-water 30-day mean dissolved oxygen criterion 
biological reference curve applicable only during the June 1 through September 30 
assessment period. 



Figure IV-3. Chesapeake Bay deep-water 30-day mean dissolved oxygen criterion 
biological reference curve. 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 














































42 


NON-SUMMER OPEN-WATER DISSOLVED OXYGEN 
CRITERIA REFERENCE CURVE 

The default reference curve, illustrated in Figure II-4 in Chapter 2, should be used in 
the assessment of the 30-day mean, open-water dissolved oxygen criteria during the 
non-summer months (January 1 through May 31 and October 1 through December 
31). The necessary biological indices and data were not available to support deriva¬ 
tion of a biologically based reference curve for open-water habitats during the 
non-summer months. 



Figure IV-4. Chesapeake Bay deep-channel dissolved oxygen criterion biological reference 
curve. 


ASSESSMENT OF DEEP-CHANNEL INSTANTANEOUS 
MINIMUM DISSOLVED OXYGEN CRITERIA 

The April 2003 Chesapeake Bay water quality criteria document provides conflicting 
guidance in the use of reference curves for assessing attainment of the four instanta¬ 
neous minimum dissolved oxygen criteria. Pages 170 to 173 in U.S. EPA 2003a 
display and discuss reference curves for migratory spawning and nursery, open- 
water, deep-water, and deep-channel criteria attainment assessment. All four sets of 
designated-use specific criteria include a use-specific instantaneous minimum crite¬ 
rion. With the exception of the deep-channel criteria (page 173 in U.S. EPA 2003a), 
none of these sections specifically describe whether a reference curve should be 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 




















43 


applied in assessing attainment of the respective instantaneous minimum criteria. 
The reader is left with the sense that the published reference curves should be 
applied to all the dissolved oxygen criteria, regardless of the stated duration. 

All four instantaneous minimum criteria for protection of the four designated uses— 
migratory spawning and nursery, open-water, deep-water, and deep-channel—protect 
against mortality from very short-term exposure to low dissolved oxygen concentra¬ 
tions (U.S. EPA 2003a). The other dissolved oxygen criteria with specific averaging 
periods (30-day, 7-day, and 1-day means) protect against impairments—including 
growth, respiration, and behavioral/avoidance—for which the impairments will not 
impact the designated use. The 2003 EPA criteria guidance stated that there were no 
“biologically acceptable exceedances of the applicable criteria” for the instantaneous 
minimum criteria, given that the impairment is death (page 151 in U.S. EPA 2003a). 

While updating the methodology for deriving the open-water and deep-water desig- 
nated-use dissolved oxygen criteria reference curves for the 30-day mean criteria 
(described above), there were times and locations in the Chesapeake Bay for which 
healthy benthic infaunal communities still existed despite exceedance of the 1 
mg liter' 1 instantaneous minimum criterion. The EPA recommends, therefore, that 
attainment assessment of the instantaneous minimum deep-channel dissolved 
oxygen criteria be conducted with the CFD methodology using the deep-channel 
biological reference curve (Figure IV-4; Appendices F and G). 


USE OF PERCENT SATURATION AS DISSOLVED 

OXYGEN CRITERIA 

Several Chesapeake Bay scientists have called for future publication of dissolved 
oxygen criteria based on percent saturation (not concentration) and for state adop¬ 
tion of such percent-saturation-based criteria into the states’ water quality standards 
regulations. They cite fisheries physiology research showing that the pressure 
gradient between the surrounding water and the blood running through the fishes’ 
gills that truly determines whether sufficient oxygen exists in the water to support 
aquatic life. For example, Dutil and Chabot (2001) reported: 

Fishes have developed several mechanisms to secure more oxygen from their 
environment in critical situations such as low oxygen availability (Hoar and 
Randall 1984). When the partial pressure of oxygen in the environment 
drops below some critical limit, however, the pressure gradient between 
blood and water may not allow the fish to deliver as much oxygen to its 
tissues as needed to meet metabolic requirements associated with ingestion, 
digestion, growth and activity. Thus critical thresholds may vary> through 
time in demersal fish species and are best described in terms of partial pres¬ 
sure of oxygen or percent saturation. 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



44 


These scientists also note that the amount of dissolved oxygen dissolved declines as 
temperature and salinity increase. For example, fully saturated freshwater at 20°C 
holds 9.28 mgliter' 1 of oxygen, but fully saturated seawater at the same temperature 
only contains 7.58 mg liter" 1 of oxygen. Seawater at 1°C can hold 11.38 mg liter' 1 of 
oxygen; at 30°C it can hold only 6.37 mg liter" 1 oxygen. As for the aquatic organ¬ 
isms, research indicates that percent saturation drives the oxygen diffusion supplying 
their respiratory demands. 

Concentration-based, not percent-saturation-based, criteria were published given the 
lack of reporting dissolved oxygen concentrations in terms of percent saturation in the 
extensive effects database used to derive the Chesapeake Bay dissolved oxygen criteria 
(U.S. EPA 2000). In addition, the lack of salinity and temperature values for each data 
point in the laboratory-based low dissolved oxygen effects database prevented calcula¬ 
tion of the concentration-based effects data into percent saturation numbers. 

Following publication of the Ambient Aquatic Life Water Quality> Criteria for Dissolved 
Oxygen (Saltwater): Cape Cod to Cape Hatteras , EPA scientists evaluated the implica¬ 
tions of recommending dissolved oxygen criteria as percent saturation versus 
concentration (U.S. EPA 2000). In an addendum to the 2000 Virginian Province salt¬ 
water dissolved oxygen criteria document, the U.S. EPA (2003b) reported: 

A standard based on percent saturation has a wide range of differences in 
partial pressure (2.14-4.01 Torr), which decreases with increasing temper¬ 
ature. The opposite is more desirable, however, since respiratory’ demand 
increases with temperature. Thus standards based on percent saturation are 
likely to overprotect during winter and potentially underprotect in summer, 
when organisms need the most oxygen. A standard based on concentration 
provides a more uniform difference in partial pressure over the temperatures 
used (2.45-2.72 Torr). Even though the range of difference is smaller, it still 
increases with temperature. Thus a standard based on absolute concentra¬ 
tion is more likely to create stable physiological conditions for animals 
throughout the year. 

Scientists from the EPA have generated a version of the EPA Virginian Province salt¬ 
water dissolved oxygen criteria as percent saturation for the State of Maine (G. 
Thursby, personal communication). At this time, however, the EPA does not have the 
scientific basis to recommend a set of Chesapeake Bay dissolved oxygen criteria in 
terms of percent saturation. 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 


45 


LITERATURE CITED 

Chesapeake Bay Program. 1989. Chesapeake Bay Monitoring Program Atlas - Volume 1: 
Water Quality and Other Physiochemical Monitoring Programs. CBP/TRS 34/89. U.S. Envi¬ 
ronmental Protection Agency, Chesapeake Bay Program Office, Annapolis, MD. 

Dauer, D.M.. M.F. Lane, and R.J. Llanso. 2005. Addendum to the Report: Development of 
Diagnostic Approaches to Determine Sources of Anthropogenic Stress Affecting Benthic 
Community> Condition in the Chesapeake Bay. Prepared for U.S. Environmental Protection 
Agency, Chesapeake Bay Program Office, by Department of Biological Sciences, Old 
Dominion University, Norfolk, VA, and Versar, Inc., Columbia, MD. 

Dutil, J. D. and D. Chabot. 2001. Impact of hypoxia on Atlantic Cod in the Northern Gulf of 
St. Lawrence, p. 51-60 In R.V. Thurston (Ed.), Fish Physiology, Toxicology, and Water 
Quality. Proceedings of the Sixth International Symposium, La Paz, Mexico, January 22-26. 
2001. U.S. Environmental Protection Agency Office of Research and Development, Ecosys¬ 
tems Research Division, Athens. GA. EPA/600/R-02/097. 372 pp. 

Hoar, W.S. and D.J. Randall. 1984. Fish Physiology. Volume X. Gills. Part A. Anatomy, Gas 
Transfer, and Acid-Base Regulation. Academic Press, 456 pp. 

U.S. Environmental Protection Agency (U.S. EPA). 2000. Ambient Aquatic Life Water 
Quality Criteria for Dissolved Oxygen (Saltwater): Cape Cod to Cape Hatteras. EPA 822- 
R-00-012. U.S. Environmental Protection Agency, Office of Water, Washington, D.C. 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality’ Criteria for Dissolved 
Oxygen, Water Clarity’ and Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. National Saltwater Criteria for Dissolved 
Oxygen: Potential Addendum to Virginian Province Saltwater Criteria for Warmer and 
Colder Waters. AED-03-113. U.S. Environmental Protection Agency, Office of Research and 
Development. National Health and Environmental Effects Laboratory, Atlantic Ecology Divi¬ 
sion, Narragansett, RI. 

U.S. Environmental Protection Agency. 2003c. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Ambient Water Quality> Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a Chesapeake Bay and its Tidal Tributaries - 2004 
Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability - 2004 Addendum. EPA 903-R-04-006. Region Ill 
Chesapeake Bay Program Office. Annapolis. MD. 


chapter iv - Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 



46 


Weisberg, S.B., J.A. Ranasinghe, D.M. Dauer, L.C. Schaffner, R.J. Diaz, and J.B. Frilhsen. 
1997. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries 
20: 149-158. 


chapter iv 


Refinements to the Chesapeake Bay Dissolved Oxygen Criteria Assessment Procedures 


47 


chapter \f 

Refinements to the 
Shallow-Water Designated-Use 
Assessment Procedures 


BACKGROUND 

Submerged aquatic vegetation (SAV) is a critically important component of the 
Chesapeake Bay ecosystem. These underwater plants provide habitat used by many 
fish and shellfish species and provide food for migratory waterfowl, while also 
improving water quality by generating oxygen, stabilizing sediment, and taking up 
nutrients. Historically, the Chesapeake Bay was once known for its extensive SAV 
beds. During the 1960s, however, much of the SAV disappeared. Poor water clarity, 
caused by excessive algal growth and high levels of suspended sediment (Dennison 
et al. 1993), was the primary factor in the decline of these beds. Both of these water 
quality impairments result from human activities in the Chesapeake watershed that 
cause excessive nutrient and sediment loadings to the Bay. 

In 2003, after consultation with the watershed jurisdictions, the EPA published water 
clarity criteria, SAV restoration goals, and shallow-water Bay grass designated-use 
delineations for the Chesapeake Bay as well as its tidal tributaries and embayments 
(U.S. EPA 2003a, 2003b). When applied as state water quality standards regulations, 
these standards define the water clarity needed in delineated shallow-water habitats 
to support SAV restoration to agreed-upon acreages. 

The water clarity criteria and SAV restoration goals were designed to define attain¬ 
ment of the shallow-water Bay grass designated use in three ways (U.S. EPA 2003a, 

2004a). First, once the targeted acreage of SAV in a given segment is reached, that 
segment is considered in attainment of the shallow-water Bay grass designated use. 

Measurement of SAV goal restoration attainment is based on annual aerial surveys 
in which the beds are photographed and mapped, acreages quantified, and the single 
best year of acreage determined. Second, if sufficient shallow-water area with the 
water clarity necessary to achieve restoration of the targeted SAV acres exists, then 
the segment is considered in attainment. These “water clarity acres” are measured by 
routinely mapping water clarity using data from the Chesapeake Bay Shallow-water 
Monitoring Program (see Chapter 7 for details). Third, if the water-clarity criteria 
were attained throughout the shallow-water designated use reaching to a specific 

chapter v • Refinements to the Shallow-Water Designated-Use Assessment Procedures 



48 


depth contour (segment-specific water clarity criteria application depth) based on the 
cumulative frequency diagram (CFD) assessment methodology, then the segment is 
also considered in attainment of this designated use. Like the water clarity acres 
approach, the CFD-based assessment would be performed using data from the 
shallow-water monitoring program (see Chapter 7 for details). 

For the 2006 Impaired Water 303(d) listing cycle, insufficient data existed to assess 
water clarity criteria attainment in nearly all of the Chesapeake Bay segments’ 
shallow-water bay grass designated-use habitats. The SAV acreages have been quan¬ 
tified for many years (annually since 1984), however, and this data collection is 
expected to continue. Thus, the 2006 assessments used SAV acreages over the three- 
year assessment period from 2001 through 2004. If the single best year of SAV 
coverage from that period exceeded the established, state-adopted SAV restoration 
goal, then the segment’s shallow-water designated use was deemed in attainment. If 
the SAV restoration goal was not attained, then the segment’s shallow-water desig¬ 
nated use was listed either as impaired (category 5) or as insufficient data (category 
3) since shallow-water monitoring data were unavailable for the segment. 

The procedures for assessing attainment of the Chesapeake Bay shallow-water 
designated use using the water clarity criteria and SAV restoration acreages, first 
published by EPA in 2003, were broadly defined and had not been extensively 
applied in the Chesapeake Bay prior to the 2006 303(d) listing cycle (U.S. EPA 
2003a, 2003b). The jurisdictions and the EPA identified and resolved many issues 
during the first baywide application. This chapter provides detailed and refined guid¬ 
ance on the assessment of the water clarity criteria and the SAV restoration goals. 
Ultimately, the chapter evaluates attainment of the shallow-water bay grass desig¬ 
nated use. This guidance replaces the applicable criteria assessment methodologies 
previously published by the U.S. EPA (2003a, 2003b, 2004a, 2004c). 


SHALLOW-WATER DESIGNATED-USE 
ATTAINMENT ASSESSMENT 

The shallow-water bay grass designated use is considered in attainment if sufficient 
acres of SAV are observed within the segment or enough acres of shallow-water 
habitat meet the applicable water clarity criteria to support restoration of the desired 
SAV acreage for that segment (U.S. EPA 2003a, 2003b). Assessment of either 
measure, or a combination of both, serves as the basis for determining attainment or 
impairment of the shallow-water bay grass designated use. 

Given SAV is the ultimate biological measure of attainment of the designated use, in 
the absence of sufficient shallow-water monitoring data necessary to determine the 
available water clarity acres or assess water clarity criteria attainment using the 
CFD-based criteria assessment procedure, the EPA recommends the States assess 
shallow-water bay grass designated use attainment/impairment based on the acres of 
aerial mapped SAV. 


chapter v 


Refinements to the Shallow-Water Designated-Use Assessment Procedures 



If a shallow-water bay grass designated use segment meets its SAV restoration 
acreage, that designated use-segment is considered in attainment of the designated 
use and should be listed on part 2. 

If a shallow-water bay grass designated use segment does not meet its SAV restora¬ 
tion acreage and sufficient shallow-water monitoring data is available, the 
jurisdiction can then assess attainment of the designated use using water clarity acres 
or water clarity criteria as described below. If the water clarity acres/water clarity 
criteria are met/attained based on assessment of spatially intensive shallow-water 
monitoring data, then that designated use-segment is considered in attainment of the 
shallow-water bay grasses designated use and should be listed on part 2. 

If the water clarity acres/water clarity criteria are not met/attained based on assess¬ 
ment of shallow-water monitoring data, or if there is insufficient data to make a 
determination using water clarity acres, then that designated use-segment is consid¬ 
ered not in attainment of the shallow-water bay grasses designated use and should be 
listed on part 5. 

For those segments that contain the shallow-water bay grass designated use, attain¬ 
ment of this use should be assessed with the following procedure: 

If the segment's single best year SAV acreage (described below) drawn 
from the most recent three-year period of available data is equal to or greater 
then the state adopted SAV restoration acreage for that segment, then that 
segment is considered to be in attainment of its shallow-water bay grass 
designated use. If the segment’s single best year SAV acreage is less than 
the state adopted SAV restoration acreage for that segment, the state should 
then proceed to assess water clarity acres if sufficient shallow-water data is 
available, otherwise, the segment is not in attainment. 

If the segment’s water clarity acres (defined below) calculated from the 
most recent three-year period of available shallow-water monitoring water 
clarity data is equal to or greater than state adopted water clarity restoration 
acreage for that segment, then that segment is considered to be in attainment 
of its shallow-water bay grass designated use. If the segment’s water clarity 
acres are less than the state adopted water clarity restoration acreage for that 
segment, then that segment is considered not to be in attainment of its 
shallow-water designated use unless SAV acreage data indicate attainment. 

A jurisdiction may also choose to apply the CFD-based assessment of water 
clarity criteria, described in more detail below, in place of water clarity acres, to 
assess attainment of the segment's shallow-water bay grass designated use. 

Given that SAV is the ultimate biological measure of attainment of the designated 
use, the EPA recommends a specific sequence of criteria assessment: assessment of 
SAV acres meeting the segment-specific restoration acres first, followed by assess¬ 
ment of water clarity acres or water clarity criteria attainment. In the absence of 
sufficient shallow-water monitoring data to determine the available water clarity 
acres or assess water clarity criteria attainment using the CFD-based procedure, the 


chapter v 


Refinements to the Shallow-Water Dcsignated-Usc Assessment Procedure 


50 


EPA recommends that the states assess shallow-water bay grass designated-use 
attainment based on the acres of mapped SAV (see Chapter 8). 

ASSESSMENT BASED ON THE SINGLE BEST YEAR OF SAV 

Baywide and segment-specific SAV restoration goals were defined for the Chesa¬ 
peake Bay by evaluating the historical (1930s- 1970s) and more recent (1980s-2000) 
SAV distributions (U.S. EPA 2003b). Historical aerial photographs, available for 
selected years in the 1930s, 1950s, and 1960s, were converted to digital maps. Then 
acreages of SAV for all photographed shallow-water areas in Chesapeake Bay, its 
tidal tributaries and embayments were quantified. To set restoration goals for the 
Bay, the single best year of SAV coverage was defined as the restoration goal for 
each segment. The combined individual restoration acreages yielded a baywide goal 
of 185,000 acres. (See pages 105-122 in U.S. EPA 2003b for detailed documenta¬ 
tion on the entire goal-setting process.) 

This baywide restoration goal was established “to reflect the historical abundance, 
measured as acreage and density from the 1930’s to present” as committed to in the 
Chesapeake 2000 agreement (Chesapeake Executive Council 2000) and essentially 
represents the “existing use” as defined by the Clean Water Act. The single-best-year 
approach was necessary because a common basis was needed to define the area of 
SAV that should be present. The historical photography was not consistent through 
time for all areas of the Bay and SAV acreages varied through time. Since at least 
some coverage was available for most of the Bay, the single best year offered the best 
option for setting goals (in selected cases with little or incomplete historical data, a 
composite of multiple years of historical data was used to define the “single best 
year”) (U.S. EPA 2003b). 

Because the segment-based SAV restoration goals were established based on the prin¬ 
ciple of a “single best year,” the assessment of attaining that goal within an individual 
Chesapeake Bay Program segment’s shallow-water bay grass designated-use habitat is 
defined in a similar manner. Attainment of the SAV restoration goal is reached when 
the single best year of SAV acreage during the applicable preceding three-year assess¬ 
ment period equals or exceeds the established goal (defined as “SAV restoration acres” 
in the states’ water quality standards regulations) for that segment. 

In nine segments, SAV restoration goals were not published in 2003 because no SAV 
was mapped in the available historical photography or through the baywide aerial 
survey (U.S. EPA 2003b). At the same time, existing information does not support 
delineation of these entire segments as SAV no-grow zones following the detailed 
decision rules documented by the U.S. EPA (2003b). The EPA recommends the 
jurisdictions maintain the shallow-water designated use in the nine segments that 
didn’t have an SAV restoration goal published in 2003 but were previously deter¬ 
mined not to be an SAV no-grow zone (Table V-l). 


chapter v 


Refinements to the Shallow Watei Designated Use Assessment Procedures 


51 


Table V-1. Recommended tidal-water designated uses by Chesapeake Bay Program segment and state-adopted sub- 
segment. Updated version of Table IV-3 originally published on pages 62-63 of the 2003 Technical Support Document for 
Identification of Chesapeake Bay Designated Uses and Attainability (U.S. EPA 2003b). The asterisks (*) indicate that no 
numerical SAV restoration acreage goal was published in 2003 for the shallow-water designated use of that segment. 

See,Table V-2 for the nine new segment numerical SAV restoration averages. The absence of an "X" in the shallow-water 
designated-use column indicates that segment has been entirely delineated as an SAV no-grow zone and the shallow- 
water bay grass designated use should not apply to that segment. 





Migratory 








Spawning 




Shallow- 




and 

Open- 

Deep- 

Deep- 

Water 




Nursery 

Water 

Water 

Channel 

(SAV 

Chesapeake Bay Program 

CBP 

Juris- 

(Feb. 1- 

(Year- 

(June 1- 

(June 1- 

growing 

^Segmen^lame 

Segment 

diction 

May 31) 

Round) 

Sept. 30) 

Sept. 30) 

season) 


Northern Chesapeake Bay 

CB1TF1 

MD 

X 

X 



X 

Northern Chesapeake Bay 

CB1TF2 

MD 

X 

X 



X 

Upper Chesapeake Bay 

CB20H 

MD 

X 

X 



X 

Upper Central Chesapeake Bay 

CB3MH 

MD 

X 

X 

X 

X 

X 

Middle Central Chesapeake Bay 

CB4MH 

MD 

X 

X 

X 

X 

X 

Lower Central Chesapeake Bay 

CB5MH 

MD 


X 

X 

X 

X 

Lower Central Chesapeake Bay 

CB5MH 

VA 


X 

X 

X 

X 

Western Lower Chesapeake Bay 

CB6PH 

VA 


X 

X 


X 

Eastern Lower Chesapeake Bay 

CB7PH 

VA 


X 

X 


X 

Mouth of the Chesapeake Bay 

CB8PH 

VA 


X 



X 

Bush River 

BSHOH 

MD 

X 

X 



X 

Gunpowder River 

GUNOH1 

MD 

X 

X 



X 

Gunpowder River 

GUNOH2 

MD 

X 

X 



X 

Middle River 

MIDOH 

MD 

X 

X 



X 

Back River 

BACOH 

MD 

X 

X 



X* 

Patapsco River 

PATMH 

MD 

X 

X 

X 


X 

Magothy River 

MAGMH 

MD 

X 

X 



X 

Severn River 

SEVMH 

MD 

X 

X 



X 

South River 

SOUMH 

MD 

X 

X 



X 

Rhode River 

RHDMH 

MD 

X 

X 



X 

West River 

WSTMH 

MD 

X 

X 



X 

Upper Patuxent River 

PAXTF 

MD 

X 

X 



X 

Western Branch (Patuxent R.) 

WBRTF 

MD 

X 

X 



X* 

Middle Patuxent River 

PAXOH 

MD 

X 

X 



X 

Lower Patuxent River 

PAXMH1 

MD 

X 

X 

X 


X 

Lower Patuxent River 

PAXMH2 

MD 

X 

X 

X 


X 

Lower Patuxent River 

PAXMH3 

MD 

X 

X 

X 


X 

Lower Patuxent River 

PAXMH4 

MD 

X 

X 

X 


X 

Lower Patuxent River 

PAXMH5 

MD 

X 

X 

X 


X 

Lower Patuxent River 

PAXMH6 

MD 

X 

X 

X 


X 

Upper Potomac River 

POTTF 

DC 

X 

X 



X 

Upper Potomac River 

POTTF 

MD 

X 

X 



X 

Upper Potomac River 

POTTF 

VA 

X 

X 



X 


continued 


chapter v 


Refinements to the Shallow Water Designated Use Assessment Procedures 























































52 


fable V-1. (continued) 


Anacostia River 

ANATF 

DC 

X 

X 



X 

Anacostia River 

ANATF 

MD 

X 

X 



X 

Piscataway Creek 

P1STF 

MD 

X 

X 



X 

Mattawoman Creek 

MATTF 

MD 

X 

X 



X 

Middle Potomac River 

POTOH1 

MD 

X 

X 



X 

Middle Potomac River 

POTOH2 

MD 

X 

X 



X 

Middle Potomac River 

POTOH3 

MD 

X 

X 



X 

Middle Potomac River 

POTOH 

VA 

X 

X 



X 

Lower Potomac River 

POTMH 

MD 

X 

X 

X 

X 

X 

Lower Potomac River 

POTMH 

VA 

X 

X 

X 

X 

X 

Upper Rappahannock River 

RPPTF 

VA 

X 

X 



X 

Middle Rappahannock River 

RPPOH 

VA 

X 

X 



X* 

Lower Rappahannock River 

RPPMH 

VA 

X 

X 

X 

X 

X 

Corrotoman River 

CRRMH 

VA 

X 

X 



X 

Piankatank River 

P1AMH 

VA 


X 



X 

Upper Mattaponi River 

MPNTF 

VA 

X 

X 



X 

Lower Mattaponi River 

MPNOH 

VA 

X 

X 



X* 

Upper Pamunkey River 

PMKTF 

VA 

X 

X 



X 

Lower Pamunkey River 

PMKOH 

VA 

X 

X 



X* 

Middle York River 

YRKMH 

VA 

X 

X 



X 

Lower York River 

YRK.PH 

VA 


X 

X 


X 

Mobjack Bay 

MOBPH 

VA 


X 

X 


X 

Upper James River 

JMSTF1 

VA 

X 

X 



X 

Upper James River 

JMSTF2 

VA 

X 

X 



X 

Appomattox River 

APPTF 

VA 

X 

X 



X 

Middle James River 

JMSOH 

VA 

X 

X 



X 

Chickahominy River 

CHKOH 

VA 

X 

X 



X 

Lower James River 

JMSMH 

VA 

X 

X 



X 

Mouth of the James River 

JMSPH 

VA 


X 



X 

Western Branch Elizabeth River 

WBEMH 

VA 


X 




Southern Branch Elizabeth River 

SBEMH 

VA 


X 




Eastern Branch Elizabeth River 

EBEMH 

VA 


X 




Lafayette River 

LAFMH 

VA 


X 




Mouth of the Elizabeth River 

ELIPH 

VA 


X 

X 

X 


Lynnhaven River 

LYNPH 

VA 


X 



X 

Northeast River 

NORTF 

VA 

X 

X 



X 

C&D Canal 

C&DOH 

DE 

X 

X 



X 

C&D Canal 

C&DOH 

MD 

X 

X 



X 

Bohemia River 

BOHOH 

MD 

X 

X 



X 

Elk River 

ELK.OH1 

MD 

X 

X 



X 

Elk River 

ELKOH2 

MD 

X 

X 



X 

Sassafras River 

SASOH1 

MD 

X 

X 



X 


chapter v 


Refinements to the Shallow Water Designated Use Assessment Procedures 





































































53 


Table V-1. (continued) 



Sassafras River 

SASOH2 

MD 

X 

X 



X 

Upper Chester River 

CHSTF 

MD 

X 

X 



X* 

Middle Chester River 

CHSOH 

MD 

X 

X 



X 

Lower Chester River 

CHSMH 

MD 

X 

X 

X 

X 

X 

Eastern Bay 

EASMH 

MD 


X 

X 

X 

X 

Upper Choptank River 

CHOTF 

MD 

X 

X 




Middle Choptank River 

CHOOH 

MD 

X 

X 



X 

Lower Choptank River 

CHOMH2 

MD 

X 

X 



X 

Mouth of the Choptank River 

CHOMH1 

MD 

X 

X 



X 

Little Choptank River 

LCHMH 

MD 


X 



X 

Honga River 

HNGMH 

MD 


X 



X 

Fishing Bay 

FSBMH 

MD 

X 

X 



X 

Upper Nanticoke River 

NANTF 

MD 

X 

X 




Upper Nanticoke River 

NANTF 

DE 

X 

X 



X* 

Middle Nanticoke River 

NANOH 

MD 

X 

X 



X 

Lower Nanticoke River 

NANMH 

MD 

X 

X 



X 

Wicomico River 

WICMH 

MD 

X 

X 



X 

Manokin River 

MANMH1 

MD 

X 

X 



X 

Manokin River 

MANMH2 

MD 

X 

X 



X 

Big Annemessex River 

BIGMH1 

MD 

X 

X 



X 

Big Annemessex River 

BIGMH2 

MD 

X 

X 



X 

Upper Pocomoke River 

POCTF 

MD 

X 

X 




Middle Pocomoke River 

POCOH 

MD 

X 

X 



X* 

Middle Pocomoke River 

POCOH 

VA 

X 

X 



X* 

Lower Pocomoke River 

POCMH 

MD 

X 

X 



X 

Lower Pocomoke River 

POCMH 

VA 

X 

X 



X 

Tangier Sound 

TANMH1 

MD 


X 



X 

Tangier Sound 

TANMH2 

MD 


X 



X 

Tangier Sound 

TANMH 

VA 


X 



X 


Source: U.S. ERA 2003b, 2004b, 2004c, 2005 


To determine attainment of the shallow-water bay grass designated use, SAV restora¬ 
tion goals for these nine segments were established based on the total surface acre 
between the shoreline and the 0.5-meter depth contour divided by the 2.5 water 
clarity acres multiplier (Table V-2). Any SAV no-grow zones within the individual 
segments were removed before conducting the above calculations. 

ASSESSMENT BASED ON WATER CLARITY ACRES 

The EPA has determined that the shallow-water designated use is protected when 
there is restoration of SAV to the targeted restoration acreages or when a sufficient 
area of shallow-water habitat contains required levels of water clarity, accounting for 


chapter v 


Refinements to the Shallow-Water Designated Use Assessment Procedures 














































54 


Table V-2. SAV restoration goals for segments that had no published acreage goals 
in 2003. 


Chesapeake 
Bay Program 
Segment 

Segment Name 

Shallow-Water 
Habitat Area' 
(Acres) 

SAV 

Restoration 

Coal 2 

(Acres) 

ANATF (MD) 

Anacostia River-Maryland 

- -y - 

3 

BACHOH 

Back River 

850 

340 

C&DOH (DE) 

C&D Canal-Delaware 

15 

6 

C&DOH (MD) 

C&D Canal-Maryland 

83 

33 

CHSTF 

Upper Chester River 

574 

230 

MPNOH 

Lower Mattaponi River 

323 

129 

NANTF (DE) 

Upper Nanticokc River-Delaware 

370 

148 

PAXMH3 

Lower Patuxent River Sub-Segment 3 

3 

3 

PAXMH6 

Lower Patuxent River Sub-Segment 6 

3 

3 

PMKOH 

Lower Pamunkey River 

423 

169 

POCOH (MD) 

Middle Pocomoke River-Maryland 

56 

22 

POCOH (VA) 

Middle Pocomoke River-Virginia 

167 

67 

RPPOH 

Middle Rappahannock River 

1,226 

490 

WBRTF 

Western Branch Patuxent River 

3 

3 


'Determined as total surface area of the segment from adjacent shoreline out to the 0.5-meter depth 
contour at mean low water minus any delineated SAV no-grow zone within the segment. 
Calculated as the shallow-water habitat area divided by 2.5 (the water clarity acres multiplier) (see 


U.S. EPA 2003a). 

3 No (or very limited) bathymetry data were available, therefore, no shallow-water habitat area or 
SAV restoration goal acreage could be calculated. 


vegetated bottom habitat. Based on the decades long record of published documen¬ 
tation on SAV light requirements (Batiuk et al. 1992, 2000; Dennison et al 1993; 
Kemp et al. 2001; U.S. EPA 2003a, 2004a), the EPA recommends that an attainment 
determination based on water clarity acres be based on 2.5 times each acre needed 
to meet the restoration goal acreage. 

A water clarity acre is defined as an acre of shallow-water bay grass designated-use 
bottom habitat, located anywhere between the 2-meter depth contour and the adja¬ 
cent shoreline inclusively, which has been observed to achieve the applicable 
salinity-regime-specific water clarity criteria. A water clarity acre cannot be defined 
within a delineated SAV no-grow zone (see pages 41-55 in U.S. EPA 2004c for 
narrative descriptions and maps of those zones). For segments in which the resultant 
water clarity acreage exceeds the total acres of shallow-water habitat from the 
shoreline out to the 2-meter depth contour, the water clarity restoration acreage will 
be set at the total acreage out to the 2-meter depth contour. 

Assessment of attaining a segment’s water clarity restoration acreage should be 
based on calculation of the arithmetic mean of the year-by-year arithmetic means of 
a month-by-month accounting of water clarity acres over the three-year SAV 
growing season assessment period. Calculation of water clarity acres should be 


chapter v 


Refinements to the Shallow-Water Designated-Use Assessment Procedures 



























55 


based on spatially intensive shallow-water monitoring turbidity data converted to Kd 
(light attenuation coefficient), interpolated as described in Chapter 2 and then 
compared to the corresponding Kd threshold assigned to each interpolator grid cell. 
The total acreage of an interpolator grid cell is added to the running total water 
clarity acres for a segment when the interpolated Kd for that cell is less than or equal 
to the Kd threshold assigned to that cell. 

The Kd value based on achieving the applicable water clarity criteria at the 2-meter 
depth will apply to all interpolator grid cells with centroids within the 2-meter to 1- 
meter depth contours. All interpolator grid cells with centroids that lie within the 
area bounded by the shoreline and the 1 -meter contour will be assigned the Kd value 
for the 1 -meter depth. 

If the segment's single best year of water clarity acres, as calculated above, is equal 
to or greater than the segment's water clarity restoration acreage, then that segment 
has attained the shallow-water bay grass designated use. If the segment’s single best 
year of water clarity acres is less than the segment’s water clarity restoration acreage, 
then the segment is in non-attainment of this designated use. 

The EPA recommends the states adopt one of two approaches to calculating water 
clarity acres. Both methodologies directly account for progress towards meeting the 
SAV restoration goal acreage and measurement of suitable shallow water habitat 
acreage necessary to support restoration of the remaining SAV beds needed to reach 
the goal acreage. 

The first methodology was originally published in the 2004 Chesapeake Bay water 
quality criteria addendum (U.S. EPA 2004a). This methodology assesses attainment 
of the shallow-water bay grass designated use in a segment through a combination 
of mapped SAV acreage and meeting the applicable water clarity criteria in an addi¬ 
tional, unvegetated shallow water surface area equal to 2.5 times the remaining SAV 
acreage necessary to meet the segment's restoration goal (SAV restoration goal 
acreage minus the mapped SAV acreage). In other words, a segment’s shallow-water 
bay grass designated use would be considered in attainment if there is sufficient 
acres of shallow-water habitat meeting the applicable water clarity criteria to support 
restoration of the remaining acres of SAV, beyond the SAV beds already mapped, 
necessary to reach that segment’s SAV restoration goal acreage. These measure¬ 
ments of SAV acreages and water clarity levels would be drawn from three years of 
data as previously described in the Regional Criteria Guidance (U.S. EPA 2003a). 

Here’s a hypothetical example of this first methodology for determining attainment 
of the shallow-water bay grass designated use using both mapped SAV acreage and 
shallow-water habitat acreage meeting the water clarity criteria. Segment X has an 
SAV restoration goal acreage of 1,400 acres. Over the past three years, SAV beds 
totaling 1,100 acres have been mapped within the segment. Therefore, the remaining 
SAV acreage necessary to meet the segment’s restoration goal is 1,400 acres 
(segment SAV restoration goal) minus 1,100 acres (SAV acres currently mapped) or 
300 acres. Beyond the currently vegetated shallow-water habitat, an additional 


chapter v 


Refinements to the Shallow-Water Designated Use Assessment Procedures 



750 acres of shallow-water habitat (2.5 multiplier times 300 acres) is needed to attain 
the water clarity criteria to determine this segment is attaining its shallow-water bay 
grass designated use. 

The second methodology directly accounts for mapped acres of SAV within the 
calculation of water clarity acres. As part of the month-by-month accounting of 
water clarity acres, over the three-year SAV growing season assessment period, 
interpolator cells containing any mapped SAV beds are counted towards the total 
water clarity acres. 

Here’s a hypothetical example of this second methodology for determining attain¬ 
ment of the shallow-water bay grass use using both mapped SAV acreage and 
shallow-water habitat acreage meeting the water clarity criteria. Segment Y has an 
SAV restoration goal acreage of 1,400 acres. Applying the 2.5 multiplier, this 
segment also has a water clarity restoration acreage of 3,500 acres. Over the past 
three years, SAV beds totaling 1,100 acres have been mapped within the segment 
each year. During each growing season’s accounting of water clarity acres, these 
1,100 acres of mapped SAV beds are directly counted towards the growing season 
arithmetic mean water clarity acreage. Therefore, accounting directly for 1,100 acres 
of mapped SAV beds as water clarity acres, an additional 2,400 acres (3,500 water 
clarity restoration acres minus 1,100 acres of mapped SAV) of shallow-water habitat 
is needed to attain the water clarity criteria to determine this segment is attaining its 
shallow-water bay grass designated use. 

ASSESSMENT BASED ON CFD-BASED WATER CLARITY 
CRITERIA ATTAINMENT 

A jurisdiction may choose to apply the CFD-based assessment of water clarity 
criteria to evaluate attainment of the segment’s shallow-water bay grass designated 
use (U.S. EPA 2003a, 2004a). To attain the designated use, the segment must meet 
the applicable water clarity criteria throughout the applicable shallow-water habitat 
(from the shoreline out to the segment-specific water clarity criteria application 
depth contour) (see Table IV-13 on pages 115-117 in U.S. EPA 2003b) over three 
SAV growing seasons, factoring in allowable exceedances using the appropriate 
salinity-regime-based biological reference curve (see Figures V-l, V-2). Chapter 2 
and Appendix B document the application of the CFD-based criteria attainment 
assessment in detail. Chapter 7 deals with the specific elements of the shallow-water 
criteria attainment assessment procedures using a CFD-based evaluation. 


SHALLOW-WATER DESIGNATED USES 
AND SAV NO-GROW ZONES 

Shoreline habitats of 2 meters or less (where SAV is never expected to grow due to 
extreme wave energy, permanent physical alterations, natural discoloration of the 
water, and no functional shallow-water habitat from river channeling) were 


Refinements to the Shallow Water Designated Use Assessment Procedures 



57 


designated as SAV no-grow zones (see pages 108-110 in U.S. EPA 2003b). In the 39 
segments with SAV no-grow zones, 31 of the segments have such zones extending over 
a portion of the segment (see Table V-l on page 42 in U.S. EPA 2004c). In these 
segments, an area delineated as an SAV no-grow zone should simply be left out of any 
assessment of shallow-water designated-use attainment based on water-clarity acres or 
on a CFD-based assessment of water clarity criteria attainment. 

In the case of the eight segments where the entire shallow-water area was delineated 
as an SAV no-grow zone (see pages 108-110 in U.S. EPA 2003b), the best available 
information indicates the shallow-water bay grass designated use is not appropriate. 
The EPA recommends that this designated use not apply to (or that it be removed 
from) any segment in which the area encompassing the entire 2 meters or less 
shallow-water habitat be delineated as an SAV no-grow zone (Table V-l). 

Table V-l is an updated version of Table IV-3 originally published on pages 62-63 
in the 2003 Technical Support Document for Identification of Chesapeake Bay 
Designated Uses and Attainability (U.S. EPA 2003b). This revised table documents 
the above-described segments that are entirely SAV no-grow zones (where the 
shallow-water bay grass designated use does not apply) or had no previously estab¬ 
lished SAV restoration goal. This table includes a list of all the Chesapeake Bay 
Program segments in the Chesapeake Bay, its tidal tributaries, and its embayments 
(U.S. EPA 2004b, 2005) as well as the sub-segments delineated by Maryland and 
Virginia (U.S. EPA 2004c). 


WATER CLARITY CRITERIA REFERENCE CURVES 

The original 2003 Chesapeake Bay water quality criteria document included biolog¬ 
ical reference curves to assess attainment of the water clarity criteria using the CFD 
methodology (see pages 173-176 and Appendix H in U.S. EPA 2003a). Those refer¬ 
ence curves were developed using data collected as part of the Chesapeake Bay 
Water Quality Monitoring Program in which the monitoring stations are located in 
open, mid-channel areas of Chesapeake Bay, its tidal tributaries, and its embay¬ 
ments. Use of the fixed-station, mid-channel water quality data was necessary even 
though these data are not necessarily representative of the Bay's shallow-water habi¬ 
tats; sufficient data more representative of the shallow-water habitats were not 
available (see Chapter 9 in Batiuk et al. 2000). 

Efforts are underway through the Chesapeake Bay Shallow-water Monitoring 
Program to collect water clarity data for use in generating more appropriate biolog¬ 
ical reference curves. These data are being collected (see Chapter 7 for additional 
detail) in the same way that shallow-water designated use areas will be assessed. The 
resulting biological reference curves will, therefore, be directly comparable to the 
CFD assessment curves (see Chapter 2 for further details). Further refinement of the 
existing published water clarity criteria biological reference curves (e.g., updating 
with more recent mid-channel data, developing four salinity-regime-based curves) is 


chapter v 


Refinements to the Shallow-Water Designated-Use Assessment Procedures 



58 


not warranted at this time given ongoing collection of more appropriate shallow- 
water data. In the interim, the EPA recommends that states assess their water clarity 
criteria using the CFD methodology which uses existing published biological refer¬ 
ence curves to define the amount and pattern of allowable criteria exceedances. 

Figure V-l illustrates the biological reference curve that states should apply in the 
CFD-based water clarity criteria assessment of tidal fresh and oligohaline segments 
with shallow-water bay grass designated uses. Figure V-2 illustrates the biological 
reference curve that should be applied in the assessment of mesohaline and polyha¬ 
line segments with shallow-water bay grass designated uses. Appendix H in this 
document provides the equations for the Chesapeake Bay water clarity criteria 
biological reference curves. Preliminary results from evaluation of limited shallow- 
water monitoring data indicate that biological reference curves generated from 
mid-channel data (Figures V-l and V-2) and those generated from shallow-water 
monitoring data (see Figure VII-11 in Chapter 7) are quite similar in overall shape 
and levels of allowable exceedances. 


—i 


Oligohaline and Tidal Fresh Monthly Clarity Biological Reference Curve 



Percent of Volume 


Figure V-1. Chesapeake Bay water clarity criterion biological reference curve for application 
to tidal fresh and oligohaline shallow-water designated-use habitats. 


chapter v 


Refinements to the Shallow Water Designated-Use Assessment Procedures 





























59 


Polyhaline and Mesohaline Monthly Clarity Biological Reference Curve 



Percent of Volume 


Figure V-2. Chesapeake Bay water clarity criterion biological reference curve for application 
to mesohaline and polyhaline shallow-water designated-use habitats. 


LITERATURE CITED 

Batiuk, R.A.. P. Bergstrom, M. Kemp, E. Koch, L. Murray, J.C. Stevenson, R. Bartleson, V. 
Carter. N.B. Rybicki, J.M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K.A. 
Moore, S. Ailstock, and M. Teichberg. 2000. Chesapeake Bay Submerged Aquatic Vegetation 
Water Quality and Habitat-Based Requirements and Restoration Targets: A Second Technical 
Synthesis. CBP/TRS 245/00 EPA 903-R-00-014. U.S. EPA Chesapeake Bay Program, 
Annapolis, MD. 

Batiuk, R.A., R. Orth, K. Moore, J.C. Stevenson, W. Dennison, L. Staver, V. Carter, N.B. 
Rybicki. R. Hickman, S. Kollar, and S. Bieber. 1992. Chesapeake Bay Submerged Aquatic 
Vegetation Habitat Requirements and Restoration Targets: A Technical Synthesis. CBP/TRS 
83/92. U.S. EPA Chesapeake Bay Program, Annapolis, MD. 

Chesapeake Executive Council. 2000. Chesapeake 2000. Chesapeake Bay Program, 
Annapolis. MD. 

Dennison. W.C., R.J. Orth, K.A. Moore, J.C. Stevenson, V. Carter, S. Kollar, P. Bergstrom, 
and R.A. Batiuk. 1993. Assessing water quality with submerged aquatic plants. Bioscience 
43:86-94. 

Kemp. W.M., R.A. Batiuk, R. Bartleson, P. Bergstrom, V. Carter, C.L. Gallegos, W. Hunley, 
L. Karrh, E. Koch, J.M. Landwehr, K.A. Moore, L. Murray, M. Naylor, N.B. Rybicki, J.C. 


chapter v 


Refinements to the Shallow-Water Designated Use Assessment Procedures 



























60 


Stevenson, and D.J. Wilcox. 2004. Habitat requirements for submerged aquatic vegetation in 
Chesapeake Bay: Water quality, light regime and physical-chemical factors. Estuaries 27: 
363-377. 

U.S. Environmental Protection Agency (U.S. EPA). 2003a. Ambient Water Quality Criteria 
for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal 
Tributaries. EPA 903-R-03-002. Region Ill Chesapeake Bay Program Office, Annapolis, 
MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a Chesapeake Bay and Its Tidal Tributaries - 2004 
Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. EPA 903-R-04-008. 
CBP/TRS 268/04. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004c. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability’ - 2004 Addendum. EPA 903-R-04-006. Region III 
Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen¬ 
tation Scheme: Revisions, Decisions and Rationales (1983-2003) - 2005 Addendum. EPA 
903-R-05-004. CBP/TRS 278-06. Region III Chesapeake Bay Program Office, Annapolis, 
MD. 


chapter v 


Refinements to the Shallow Water Designated Use Assessment Procedures 


61 


cha pter \/i 

Chlorophyll a Criteria 
Assessment Procedures 


STATE WATER QUALITY STANDARDS 

With publication of the April 2003 Ambient Water Quality> Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Trib¬ 
utaries , the EPA provided the states with a recommended narrative (non-numerical) 
chlorophyll a criterion applicable to all of the Chesapeake Bay and its tidal tributary 
waters (Table VI-1) (U.S. EPA 2003). From 2004 through early 2006, Virginia and 
the District of Columbia adopted numerical chlorophyll a criteria for application in 
the tidal James River (Virginia) and across all the District’s jurisdictional tidal 
waters. Both jurisdictions determined that algae-related designated use impairments 
would likely persist in these tidal waters even after attainment of applicable 
dissolved oxygen and water clarity criteria. The technical information supporting 
adoption of numerical chlorophyll a criteria by Virginia and the District was 
published in the 2003 Chesapeake Bay water quality criteria document (U.S. EPA 
2003). Maryland and Delaware adopted narrative chlorophyll a criteria into their 
water quality standards regulations (Table VI-1). 


Table VI-1. Chesapeake Bay narrative chlorophyll a criteria. 

Concentrations of chlorophyll a in free-floating microscopic aquatic plants (algae) shall not 
exceed levels that result in ecologically undesirable consequences—such as reduced water 
clarity, low dissolved oxygen, food supply imbalances, proliferation of species deemed 
potentially harmful to aquatic life or humans or aesthetically objectionable conditions— 
or otherwise render tidal waters unsuitable for designated uses. 

Source: U.S. EPA 2003. 


chapter vi 


Chlorophyll a Criteria Assessment Procedures 





62 


CHLOROPHYLL A CRITERIA ASSESSMENT 

PROCEDURES 

CHLOROPHYLL A CRITERIA REFERENCE CURVE 

To assess attainment of the State adopted numerical chlorophyll a concentration- 
based criteria, it was necessary to establish a reference curve for use in the CFD 
criteria attainment assessment process (U.S. EPA 2003). In the case of chlorophyll 
a criteria where a biologically-based reference curve is not available, EPA recom¬ 
mends the states use of the default reference curve described in Chapter 2 (see Figure 
II-4 and Equation 1). 

CHLOROPHYLL A CRITERIA ASSESSMENT 

A criterion threshold is a concentration that should rarely be exceeded by a “popu¬ 
lation” of concentration data exhibiting healthy levels. The state-adopted 
concentration-based chlorophyll a criteria values are threshold concentrations that 
should only be exceeded infrequently since a low number of naturally occurring 
exceedances occur even in a healthy phytoplankton population. The assessment of 
chlorophyll a criteria attainment, therefore, should use the CFD-based assessment 
method described in Chapter 2 that applies the default reference curve. These 
Chesapeake Bay chlorophyll a criteria apply only to those seasons and salinity-based 
habitats for which they were defined to protect against applicable human health 
and aquatic life impairments. Each season—spring (March 1-May 31) and summer 
(July 1-September 30)—should be assessed separately to evaluate chlorophyll a 
criteria attainment. 

Assessments of seasonal mean chlorophyll a criteria should be based on seasonal 
averages of interpolated data sets. To calculate the seasonal averages, each interpo¬ 
lated cruise within a season should be averaged on a point-by-point basis in 
matching interpolator grid cells. Spatial violation rates should be calculated for each 
seasonally aggregated interpolation in an assessment period. For example, for a 
summer open-water seasonal chlorophyll a criteria assessment of a three-year 
assessment period, three seasonal average interpolations representing each season 
(Year 1 Summer, Year 2 summer, Year 3 summer) should be used. 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 


chapter vi 


Chlorophyll a Criteria Assessment Procedures 




63 


chapter \/ii 


Shallow-water Monitoring and 
Application for Criteria 
Assessment 


DESIGN AND APPROACH FOR CHESAPEAKE BAY 
SHALLOW-WATER MONITORING 

In July 2001, the Chesapeake Bay Program Monitoring and Analysis Subcom¬ 
mittee’s Tidal Monitoring and Analysis Workgroup formed a Tidal Monitoring 
Design Team that undertook the redesigning of the Chesapeake Bay Tidal Moni¬ 
toring Network. Over the next two years, the Design Team set goals and objectives, 
reviewed the existing Chesapeake Bay monitoring design, evaluated potential new 
monitoring strategies, and made recommendations for implementing a network to 
provide the requisite data and support to address the Chesapeake Bay Program's 
programmatic goals and objectives. 

The new Tidal Monitoring Network focused on meeting the water quality protection 
and restoration goals and objectives of the Chesapeake 2000 agreement (Chesapeake 
Executive Council 2000). The network’s primary objective is to supply the water 
quality monitoring information needed to assess the new water quality criteria for 
dissolved oxygen, water clarity, and chlorophyll a — ultimately with the goal of 
removing the Chesapeake Bay and its tidal rivers from the list of impaired waters. 
Secondary network objectives are to provide information for defining the nutrient 
and sediment conditions necessary for protecting living resources and vital habitats. 
Water quality data would also support refinement, calibration, and validation of the 
Chesapeake Bay Water Quality/Sediment Transport Model. 

The design of the new Tidal Monitoring Network emphasized monitoring of the 
shallow-water designated use areas. In a 1999 study, the Maryland Department of 
Natural Resources investigated the validity of using mid-channel data to assess 
nearshore areas. The 13-tributary study examined water quality at 127 nearshore 
stations and compared the data to 54 adjacent mid-channel stations (Karrh 1999; 
Batiuk et al. 2000). The study found wide variations between nearshore and mid¬ 
channel data, both within and between tributaries. Based on this finding, the 
researchers concluded that decisions to use mid-channel data to characterize 
nearshore conditions should be made on a site-by-site basis. Figure VII-1 illustrates 


chapter vii 


Shallow water Monitoring and Application for Criteria Assessment 



64 


this variability, showing situations in which a single, mid-channel data point would 
not adequately represent suspended solids and chlorophyll a in shallow areas. The 
Design Team concluded that monitoring of shallow, nearshore waters must have 
greater spatial coverage to obtain an accurate representation of these parameters. 


A 



Turbidity 

□ 0.0 -5.0 [Z 10.0-15.0 E3 20.0 -25.0 M 30.0 - 40.0 ■ 50.0 - 60.0 
LJ 5.0-10.0 L_ 15.0 -20.0 B3 25.0 - 30.0 ■40.0-50.0 ■>60.0 


B 



J»mo t ft 


JMSTF 


Chlorophyll 

LJ 0.0 - 5.0 LJ 10.0 - 1 5.0 LJ 20.0 - 25.0 30.0 - 40.0 ■ 50.0 - 60.0 

LJ 5.0-10.0 LJ 15.0-20.0 13 25.0-30.0 B 40.0 - 50.0 B >60.0 


Figure VI1-1. Spatial distribution of turbidity (A) and chlorophyll a (B) in the tidal James River. 


Source: Virginia Institute of Marine Science—www2.vims.edu/vecos/ 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 














65 


To capture the temporal variability of dissolved oxygen, the new Tidal Monitoring 
Network incorporated high-frequency monitoring stations in surface and nearshore 
locations. Since then, the dissolved oxygen criteria assessment procedure has been 
modified to project the results of open-water dissolved oxygen assessments onto 
adjacent shallow-water, designated-use areas, instead of conducting a separate 
shallow-water assessment (see Chapter 4 for details). The design for collecting high- 
frequency dissolved oxygen data will likely be modified to represent dissolved 
oxygen concentrations in open-water, designated-use habitats more accurately. 

SHALLOW-WATER MONITORING DESIGN 

The intensive shallow-water monitoring program design is based on two innovative 
technologies that were extensively tested in Maryland's Magothy and Severn rivers 
as well as Tangier Sound from 1999 to 2002. The Dataflow water quality mapping 
component collects high-resolution surface data from both open tidal-tributary and 
shallow waters. The shallow-water buoy system collects high-frequency (near- 
continuous) temporal data at specific locations, resulting in a data set that better 
represents dissolved oxygen, chlorophyll a , and water clarity in time and space in 
smaller tidal tributaries, small embayments, and shallow-water habitats. In 2003, the 
Maryland Department of Natural Resources, the University of Maryland’s Chesa¬ 
peake Biological Laboratory, the Virginia Department of Environmental Quality, and 
the Virginia Institute of Marine Sciences initiated the new Chesapeake Bay Shallow- 
water Monitoring Program. The two states and their partners closely coordinate 
development of the monitoring schedules, equipment, methodologies, and quality 
assurance procedures to ensure baywide compatibility and comparability. 

The Shallow-water Monitoring Program is based on two components that collect 
spatially and temporally intensive data. Known as “Dataflow,” the spatially intensive 
component includes a sensor array and a GPS system that provide data continuously 
along a boat track in both shallow- and open-water designated-use areas. These data 
can be used to develop detailed maps of water quality conditions. The temporally 
intensive component is known as “continuous monitoring” and includes a sensor array 
at fixed locations that provides data continuously through time. These data reflect 
episodic changes in water quality or signify extremes in water quality conditions. 

The existing shallow-water monitoring design is based upon a three-year assessment 
period. Data are collected from all segments within a tidal tributary or embayment 
during the same three years. Both Dataflow sampling and continuous buoys are 
deployed for the same time period. The three-year assessment provides adequate 
time to account for variation in both weather and hydrologic conditions (see page 
151 in U.S.EPA 2003a). Assessments using fewer than three years of shallow-water 
monitoring data are discussed in the section Schedule for Assessment of Shallow- 
water Designated Use Habitats below. 

To adequately assess water quality criteria in shallow-water habitats and tidal tribu¬ 
tary open-water designated-use habitats, the EPA recommends that the states 


chapter vii 


Shallow water Monitoring and Application for Criteria Assessment 


66 


conduct Dataflow monitoring from April through October in tidal fresh, oligohaline, 
and mesohaline segments and from March through November in polyhaline 
segments. These assessment periods for the water clarity criteria were based on the 
growing seasons for the salinity-based SAV plant communities (U.S. EPA 2003a). 

CONTINUOUS MONITORING COMPONENT 

Continuous monitoring data are collected to assess the variability of water quality 
parameters throughout the day. Temporally intensive data help explain the relation¬ 
ships and timing among algal blooms, low dissolved oxygen, and nutrient additions. 
Although previous convention suggested that shallow-water habitats did not experi¬ 
ence significant low dissolved oxygen levels, continuous monitoring data are 
proving otherwise. The lowest dissolved oxygen levels often occur between 4:00 and 
6:00 a.m. when, historically, little information has been collected. 

The continuous monitoring program component employs automated YSI 6600 EDS 
water quality data sondes. Maryland and Virginia have agreed to use similar instru¬ 
ments, when possible, to ensure consistent methodology and comparability across 
Chesapeake Bay segments. The YSI 6600 sonde directly measures dissolved oxygen, 
fluorescence (an indication of chlorophyll a), turbidity (an indication of water 
clarity), temperature, salinity, and pH. The Maryland Department of Natural 
Resources Chesapeake Bay Shallow-water Monitoring Program Quality Assurance 
Project Plan (see page 32 in Maryland Department of Natural Resources 2006) docu¬ 
ments the YSI instrument parameters, range, resolution, units, and accuracy. 
Fluorescence is correlated to chlorophyll a , the measurement used for assessing 
attainment of the chlorophyll a criteria. Turbidity is correlated to K d (light attenuation 
coefficient), the measurement used to assess attainment of the water clarity criteria. 

The initial design recommended two shallow-water buoy deployments in each 
segment, but often, resources limit the number of buoys to one per site. The buoys 
are programmed to take measurements every 15 minutes for the six parameters listed 
above. They are deployed off piers or pylons, either 1-meter below the surface or at 
a fixed depth of 0.3 meters above the bottom, generally in waters of 2-meters or less 
in depth (Figure V1I-2). 

Instruments are exchanged every one to two weeks, depending on biofouling and 
following strict calibration protocols (Virginia Institute of Marine Science 2005). 
Field crews collect samples to calibrate fluorescence and turbidity instrument read¬ 
ings, respectively, with chlorophyll a and light attenuation. The monitors are 
positioned at representative sites both up- and down-river. 

Both Maryland and Virginia have rigorous shallow-water monitoring quality assur¬ 
ance/quality control (QA/QC) programs. The QA/QC protocols remain consistent 
between states; the Chesapeake Bay Program Quality Assurance Officer and the 
Chesapeake Bay Program’s Analytical Methods and Quality Assurance Workgroup 
have reviewed these protocols. 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 


67 



Figure VII-2. Example of a continuous monitoring site and the generated 2004 dissolved oxygen data record 
at Fenwick Point in the Potomac River, Maryland. 

Source: Maryland Department of Natural Resources — www.eyesonthebay.net 


Overlap periods occur at each continuous monitoring site by using multiple sondes 
during routine biweekly maintenance runs to determine instrument drift. Instruments 
are pre- and post-calibrated and must meet rigorous QA/QC protocols. Two instru¬ 
ments are dedicated to each site. When one instrument is removed from the site for 
maintenance, it is measured against the newly calibrated instrument. At the same time, 
a field crew member takes a full suite of calibration samples for laboratory analysis. 

Satellite and cellular telemetry are implemented at a subset of continuous monitoring 
sites where resources permit. Data from these sites are assessed on a daily basis. 
Maryland shallow-water continuous monitoring data are available in near- or real¬ 
time on the Department of Natural Resources “Eyes on the Bay” website 
(www.eyesonthebay.net) (Figure VII-3). Virginia shallow-water continuous moni¬ 
toring data are available on the Virginia Institute of Marine Sciences website 
(www2.vims.edu/vecos/). The Chesapeake Bay Program website’s data hub 
(www.chesapeakebay.net/data) offers access to the complete quality assured 
Shallow-water Monitoring Program datasets for Maryland and Virginia. 

WATER QUALITY MAPPING COMPONENT 

The main purpose for collecting high-resolution water quality data is to provide reli¬ 
able water quality criteria assessments. However, Dataflow monitoring also provides 
insight into spatial complexities and localized phenomena and information for water 
quality modeling in shallow waters (STAC 2005). The data are useful in producing 
maps of the extent and patchiness of algal blooms, seasonal and inter-annual 
progressions, and localized water quality impairments. 

The Dataflow system is a small, fast-moving vessel that pumps surface water contin¬ 
uously from 0.5 meters below the water surface through a chamber surrounding the 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 

















68 


Click Stations for Data 


•NT 

r 



y . 

° *■& 


Click Legend Symbol* lo 
Toggle Stallone On Ott 

Fixed Monthly 
Station* - Current & 
Historical Data 
Real-Time 

Continuous Monitor* 
Near-Time 

Continuous Monitors 


• Continuous Monitors 
Coming Soon! 


Water Ouallty 
Mapping 

2005 Current Algal 
Bloom Events 



Figure VII-3. The 2005 fixed dataflow and continuous monitoring station map from the 
Maryland Department of Natural Resources' "Eyes on the Bay" website. 

Source: Maryland Department of Natural Resources — www.eyesonthebay.net. 


probes of a YSI 6600 sonde (http://mddnr.chesapeakebay.net/sim/dataflow_instrumen- 
tation.cfm). The system uses the same YSI 6600 sonde as the continuous monitoring 
buoys and measures the same suite of six parameters—dissolved oxygen, fluorescence, 
turbidity, temperature, salinity, and pH. A Global Positioning System (GPS) unit is inte¬ 
grated into the computer system to measure the spatial position of each recorded 
measurement. Data are collected every four seconds as the boat follows a cruise track 
that traverses between shallow and open waters. These data are then interpolated to 
provide a high-resolution map of surface water quality conditions (see Chapter 2 for 
further details). Each segment is mapped monthly from April through October or March 
through November. The vessel stops at different locations throughout a segment for 
discrete measurements of photosynthetically active radiation (PAR), Secchi depth, and 
dissolved oxygen along with collection of water samples for laboratory analysis of 
chlorophyll a (for use as calibration data). These “calibration” sites often overlap with 
existing open-water fixed-station sites and continuous monitoring sites; they represent 
the dynamic range of water quality in that segment. 


SCHEDULE FOR ASSESSMENT OF SHALLOW-WATER 
DESIGNATED-USE HABITATS 

The current level of shallow-water monitoring is insufficient to conduct detailed 
water quality criteria assessments in all Chesapeake Bay shallow-water habitats by 
the Chesapeake 2000 agreement deadline of 2010 (Chesapeake Executive Council 
2000). Three possible actions might remedy this problem. The first is extending the 
deadline beyond 2010 for assessment of all Bay shallow-water habitats. The second 


chapter vii 


Shallow water Monitoring and Application for Criteria Assessment 









69 


is identifying additional resources to expand the monitoring needed to meet the 2010 
deadline. The third option is assessing segments for fewer than three years if 
noncompliance of the segment is established. All three options are addressed below. 
Accurately assessing how many segments can be assessed by each action remains 
impossible however, since determining the availability of additional resources or 
establishing how many segments might need fewer than three years of monitoring if 
noncompliance is established cannot be predicted. 

EXTENDING THE TIMEFRAME 

The Chesapeake Bay Program partners have not approved extending the shallow- 
water clarity criteria assessment timeframe beyond 2010. The current deadline will 
not be met due to a lack of adequate resources to implement the shallow-water moni¬ 
toring program design agreed upon by the Chesapeake Bay Program and the 
participating states and thoroughly reviewed by the Chesapeake Bay Program Scien¬ 
tific Technical Advisory Committee (STAC 2005). Significant progress has been 
made to accelerate the assessment schedule. Although intensive shallow-water moni¬ 
toring water clarity monitoring data will not be available in all segments, attainment 
of the shallow-water bay grass designated use for those segments that contain an 
SAV restoration acreage would be assessed by comparing each segment’s single-best 
SAV acreage from the most recent three-year period with the jurisdiction’s adopted 
segment-specific SAV restoration acreage (see Chapters 5 and 8 for further details). 
In this way, each shallow-water designated-use segment could have some assessment 
completed each year. 

ADDITIONAL RESOURCES 

Maryland, Virginia, and the EPA are actively seeking additional resources to expand 
shallow-water monitoring in order to accelerate the schedule for completing baywide 
assessments. In 2003, when Maryland and Virginia implemented shallow-water 
monitoring in 1 1 Maryland segments and seven Virginia segments, it was estimated 
that it would take until 2018 to assess all 78 Chesapeake Bay Program segments over 
a three-year period on a rotating basis. Since the Shallow-water Monitoring 
Program’s initial implementation, both Maryland and Virginia have developed part¬ 
nerships with county governments (e.g., Anne Arundel and Harford counties in 
Maryland), municipal agencies (e.g., Hampton Roads Sanitation District in 
Virginia), and federal agencies (e.g., NOAA’s National Estuarine Research System) 
and secured additional state funding to accelerate monitoring of all segments. Based 
on these new partnerships, current expanded resources, and segment assessment over 
a three-year period, it is estimated that Maryland will complete all its shallow-water 
assessments by the year 2014 and Virginia will complete all its shallow-water assess¬ 
ments by 2015. Figures VII-4 and VII-5 depict the current tentative schedule for 
shallow-water monitoring and assessment of Maryland and Virginia segments, 
respectively. New sources of funding continue to materialize and the schedules indi¬ 
cated by Figures VII-4 and VII-5 will change in response to funding adjustments. 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 



Year Assessment Complete 


] 2006 


2010 



J 2007 


2011 



Hi 2008 


2012 



2009 


2014 


Segments 


BACOH 

BIGMH 

BOHOH 

BSHOH 


MATTF 

MIDOH 


] C&DOH 
] CBlTF 
] CB20H 
] CB3MH 
] CB4MH 
1 CB5MH 
CHOMH1 
CHOMH2 
CHOOH 
CHOTF 
CHSMH 
CHS OH 
CHSTF 
EASMH 

elkoh 

FSBMH 

GUNOH 

HNGMH 

LCHMH 

MAGMH 

MANMH 


| | NANMH 

| | NANOH 

[ | NANTF 

I I NORTF 


] PATMH 
1 PAXMH 


| | PAX OH 
| | PAXTF 


PISTF 
IPOCMH 


j POCOH 
3 POCTF 


gffgg POTMH 
POTOH 
J POTTF 
] RHDMH 
] SASOH 
] SEVMH 
] SOUMH 
J TANMH 
1 WBRTF 


I I WICMH 
I WSTMH 


Figure VII-4. Schedule for shallow-water monitoring of Maryland's Chesapeake Bay segments. 

Source: Maryland Department of Natural Resources 


ASSESSMENTS BASED ON REDUCED MONITORING 

The three-year assessment period was established to account for inter-annual variations 
in weather and hydrologic conditions (U.S. EPA 2003a). If conditions are seriously 
degraded, a state having fewer than three years of data can establish noncompliance by 
applying the CFD-based criteria assessment methodology as follows. 

First, at the start of a segment’s shallow-water monitoring, assume 100 percent 
compliance in all three years of the coming assessment period. Second, after the first 
year of monitoring, a state should develop a CFD based on the collected data, 
assuming all other planned sampling dates for the next two years had 100 percent 
compliance with the applicable criterion. Finally, if the resultant assessment CFD 
indicates that the segment will be in violation (compared to the applicable reference 
CFD) no matter what happens in the following two years, then conclude that the 
segment is out of compliance for the full assessment period and move the Shallow- 
water Monitoring Program to another segment. 


Shallow water Monitoring and Application for Criteria Assessment 















































































































71 








YEAR ASSESSMENT 
COMPLETE 


Figure VII-5. Schedule for shallow-water monitoring of Virginia's Chesapeake Bay segments. 

Source: Maryland Department of Natural Resources 

To illustrate this approach, two hypothetical scenarios are illustrated below. In the 
first example (Figure VII-6), it is assumed that monitoring was conducted for one 
year and that full attainment was achieved during all scheduled sampling dates over 
the next two years. The shallow-water monitoring over the first year indicated that 
on all of the dates, the applicable criterion was violated in 15 percent or more of the 
segment. The CFD indicates that the segment would be in noncompliance even if all 
future sampling dates had 100 percent compliance. In this case, the state could have 
decided to move the monitoring effort to a new shallow-water segment even after a 
single year of study. 

In the second example (Figure VII-7), the same assumptions are made and moni¬ 
toring is conducted over one year. In this case, however, criteria exceedance is much 
less extensive spatially and the CFD indicates that full compliance could be possible 
if the current level of attainment is found in future monitoring. Since neither compli¬ 
ance nor noncompliance could be established during the first year, shallow-water 
monitoring would need to continue. The same analysis could take place after the 
second year of monitoring and the decision could be revisited. It may turn out that a 
full three years of monitoring are necessary to determine if the segment remained in 
full compliance. 

Although determining noncompliance in fewer than three years works in theory, the 
yearly segment data must be analyzed in time to adequately design and implement a 
sampling scheme for a new segment. The states must have the flexibility to deploy 


chapter vii 


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72 


Cruise 

Cruise 

Cruise 

% of 

% of 

Ref 

Number 

Year 

Month 

Space 

Time 

Curve 




1 

0.00 

0.00 

1 

1 

May 

0.7 

0.05 

0.02 

2 

1 

June 

0.45 

0.11 

0.05 

3 

1 

July 

0.3 

0.16 

0.09 

4 

1 

August 

0.25 

0.21 

0.11 

5 

1 

September 

0.2 

0.26 

0.14 

6 

1 

October 

0.15 

0.32 

0.19 

7 

2 

May 

0 

0.37 

1.00 

8 

2 

June 

0 

0.37 

1.00 

9 

2 

July 

0 

0.37 

1.00 

10 

2 

August 

0 

0.37 

1.00 

11 

2 

September 

0 

0.37 

1.00 

12 

2 

October 

0 

0.37 

1.00 

13 

3 

May 

0 

0.37 

1.00 

14 

3 

June 

0 

0.37 

1.00 

15 

3 

July 

0 

0.37 

1.00 

16 

3 

August 

0 

0.37 

1.00 

17 

3 

September 

0 

0.37 

1.00 

18 

3 

October 

0 

0.37 

1.00 



Figure VI1-6. Scenario 1: noncompliance established after one year of shallow-water 
monitoring. 


resources to different systems. Often, implementation of a monitoring program for a 
segment requires the coordination of various stakeholders and potential partners, the 
leveraging of resources, and the allocation of field crews. 

The Chesapeake Bay Program’s Scientific and Technical Advisory Committee has 
recommended that the tributary systems be assessed in their entirety for the full 
three-year period rather than evaluating individual segments of a tributary in 
different years (STAC 2005). This recommendation is particularly important for the 
larger tidal tributaries such as the Patuxent, Potomac, Rappahannock, York, and 
James rivers. These systems have tidal fresh, oligohaline, mesohaline, and poly¬ 
haline segments, all of which influence each other. To understand the vast ecosystem 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 






















73 


Cruise 

Cruise 

Cruise 

% of 

% of 

Ref 

Number 

Year 

Month 

Space 

Time 

Curve 




1 

0.00 

0.00 

1 

1 

May 

0.4 

0.05 

0.06 

2 

1 

June 

0.2 

0.11 

0.14 

3 

1 

July 

0.12 

0.16 

0.23 

4 

1 

August 

0.09 

0.21 

0.29 

5 

1 

September 

0.05 

0.26 

0.44 

6 

1 

October 

0.03 

0.32 

0.57 

7 

2 

May 

0 

0.37 

1.00 

8 

2 

June 

0 

0.37 

1.00 

9 

2 

July 

0 

0.37 

1.00 

10 

2 

August 

0 

0.37 

1.00 

11 

2 

September 

0 

0.37 

1.00 

12 

2 

October 

0 

0.37 

1.00 

13 

3 

May 

0 

0.37 

1.00 

14 

3 

June 

0 

0.37 

1.00 

15 

3 

July 

0 

0.37 

1.00 

16 

3 

August 

0 

0.37 

1.00 

17 

3 

September 

0 

0.37 

1.00 

18 

3 

October 

0 

0.37 

1.00 



Figure VII-7. Scenario 2: noncompliance not established after one year of shallow-water 
monitoring. 


complexities and interactions between adjacent segments of a single tributary, it is 
imperative to assess these tidal tributaries and segments as whole systems and not 
discontinue monitoring in one segment if noncompliance occurs after only a year or 
two of assessment. 

The states should make the decision whether to continue shallow-water monitoring 
for the full three years or to move the monitoring to another segment after a year or 
two of sampling. In making such decisions, the state should consider the need to 
gather shallow-water data for the assessment of multiple criteria (dissolved oxygen, 
water clarity, and chlorophyll a) as well as other uses of the data (e.g., shallow-water 
water quality model development and calibration). The states will also need to 


chapter vii - Shallow-water Monitoring and Application for Criteria Assessment 





















74 


consider if it makes sense (in terms of leveraging resources, coordinating, and under¬ 
standing the relationship between segments and restoration activities) to discontinue 
a segment’s monitoring after one or two years if noncompliance of the segment is 
shown. Finally, in the case of segments crossing two or more jurisdictional bound¬ 
aries, all affected states will be involved in any decision to discontinue monitoring 
prior to the end of the full three-year assessment period. 

Importantly, the scenario described above and illustrated in Figure VII-6 does not 
form an assessment that is lower in quality than one based on three years data. Non- 
compliance is clearly established; that status would not change no matter what takes 
place in ensuing years. The same approach may not be viable using alternative 
assessment strategies such as the water clarity-acres approach for the clarity criteria. 
Since the water clarity-acres assessment method relies on the mean of three years of 
data, non-compliance could not be established in fewer than three years. The reverse, 
however, may be true. However, if the segment's SAV restoration acreage goal was 
attained during any single year, then compliance would be established and the deci¬ 
sion could be made to discontinue monitoring. 

SEGMENT PRIORITIZATION SCHEDULE 

The states’ prioritization schedule for assessing shallow water monitoring segments, 
(Figures VII-4 and VII-5) is based on several criteria—SAV coverage, maximization 
of resources, partnerships, and management needs such as dissolved oxygen criteria. 
Segment prioritization through SAV coverage is based on assessing segments that 
are close to meeting the state-adopted SAV restoration acreage goal for the indi¬ 
vidual segment. All states have agreed to assess attainment by each segment’s 
single-best SAV acreage for the most recent three-year period with the jurisdiction’s 
adopted segment-specific SAV restoration acreage (see Chapter 5 for further 
details). Many Chesapeake Bay segments range between 50 and 100 percent of 
meeting their restoration goals. 

Appendix G lists all the Delaware, Maryland, Virginia, and the District of Columbia 
segments and their relative success (by percent) in reaching their respective state- 
adopted SAV restoration acreages. Those segments that have already met their SAV 
restoration acreages constitute a lower priority for shallow-water assessment. 
Segments that have not achieved any acres in meeting their SAV restoration acreage 
form a lower priority as well. The higher the percentage attainment in meeting a 
segment’s SAV restoration acreage, the greater the priority was given for assessing 
the shallow waters of that segment. 

On the states’ 2006 303(d) lists, eight Maryland segments and six Virginia segments 
have met their adopted SAV restoration acreages. The segments that have already 
attained their shallow-water designated use are low priority for shallow-water assess¬ 
ment. Fourteen Maryland segments and five Virginia segments range between 50 and 
100 percent of meeting their SAV restoration acreages (Appendix 1). These segments 


chapter vii • 


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75 


were granted the highest priority for shallow-water monitoring (see Figures VII-4 
and VII-5). 


DISSOLVED OXYGEN CRITERIA ASSESSMENTS 
USING SHALLOW-WATER MONITORING DATA 


The Chesapeake Bay Shallow Water Monitoring Program has provided unprece¬ 
dented volumes of spatially and temporally intensive Chesapeake Bay, tidal 
tributary, and embayment data to assess water quality criteria attainment. This 
wealth of data, however, provides new and unique analytical challenges within the 
regulatory framework. In the case of dissolved oxygen criteria, these challenges 
include: temporal variation of water quality parameters, spatial interpolations, and 
scaling and interpolation issues. Specific procedures for evaluation of the 7-day, 1- 
day, and instantaneous minimum open-water and deep-water dissolved oxygen 
criteria have not been fully developed at this time. 

The assessment of the 30-day mean dissolved oxygen criteria for open-water desig- 
nated-use habitats will rely on mid-channel fixed station data combined with 
Dataflow and Dataflow calibration profile data. As noted previously, the Dataflow 
vessel stops at five to eight locations throughout a segment to collect calibration 
measurements. Dissolved oxygen is measured from the surface to the bottom at these 
sites using the same procedure as the mid-channel data collection. The dissolved 
oxygen calibration data will provide an additional day of dissolved data each month, 
at five locations instead of one or two. The dissolved oxygen Dataflow and the corre¬ 
sponding Dataflow dissolved oxygen calibration data will be interpolated and 
analyzed, along with fixed-station dissolved oxygen data, using the Chesapeake Bay 
Program's interpolator and the CFD approach described in Chapter 2. 

TEMPORAL VARIATION 

Dataflow cruises collect between 3,000 and 10,000 points over several hours in a 
segment. Data are normally collected between 7:00 a.m. and 5:00 p.m. with the boat 
traversing open and shallow waters on one side of a tidal tributary or embayment and 
repeating the process on the opposite side. The measurements can be interpolated to 
produce a continuous surface of data that can be evaluated for the percentage area of 
a segment that fails the applicable criterion. 

The diel patterns of surface dissolved oxygen are well documented in both the liter¬ 
ature and continuous monitoring data (www.eyesonthebay.net). In summer, 
dissolved oxygen normally declines to its lowest level during the early morning 
hours (3:00 a.m.) when algal and plant communities have been respiring throughout 
the night; it reaches its peak in mid-afternoon (3:00 p.m.) following photosynthetic 
activity. In some cases, this diel fluctuation can reach more than 15 mgliter' 1 


chapter vii 


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76 


dissolved oxygen. When interpolating water quality mapping data collected 
throughout the day, this variability presents a potential problem that is best illus¬ 
trated by a map. Figure VII-8 shows that data collected early in the morning on one 
side of the Severn River in Maryland is substantially lower than data collected later 
in the day on the other side. If these measures were interpolated, it would appear that 
one side of the river is faring more poorly than the other when, in fact, the dichotomy 
merely represents a temporal artifact. 

To produce a more representative spatial interpolation of surface dissolved oxygen 
data, estimating the diel dissolved oxygen trend from continuous monitoring instru¬ 
ments and using that trend estimate to adjust the Dataflow dissolved oxygen may 
prove more feasible. The University of Maryland investigated this procedure by 
comparing data from a nearshore continuous meter with those from a mid-channel 
continuous buoy. They found that the dissolved oxygen in the two locations 
responded differently to the local habitats and that nearshore dissolved oxygen 
dropped at night and the mid-channel dissolved oxygen was highly variable, often 
exceeding dissolved oxygen saturation during the day. Although the adjustment 
procedure improved the data set, the prediction error was high. Further research is 
needed to integrate the spatial and temporal monitoring data. 



Figure VII-8. Illustration of rising dissolved oxygen concentrations during the day 
(June 28, 2001) in the Severn River, Maryland. . 


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77 


SCALING AND INTERPOLATION ISSUES 

The frequency and spatial coverage of water quality sampling will always remain 
lower in relation to the temporal and spatial scales at which estuarine phenomena 
occur. To overcome this reality, researchers must use innovative sample designs and 
statistical methods. Throughout the Chesapeake Bay Program’s tidal data analysis 
and monitoring network design meetings, many of these issues regarding the inter¬ 
pretation of shallow water monitoring data have been raised, but all were not solved. 
The major issues relating to dissolved oxygen are highlighted below. 

Water quality mapping of dissolved oxygen uses measures from a half-meter below 
the surface. Some consider this type of measurement a weakness given that most 
hypoxic events occur in deep-water or deep-channel habitats. The last five years of 
water quality mapping, however, have revealed that hypoxic events can affect surface 
and shallow waters more than initially recognized. Each mapping cruise collects 
calibration samples and water quality depth profiles at five to eight stations per 
segment. In much the same fashion that fixed station profiles are interpolated in 
three-dimensions using the Chesapeake Bay interpolator (see Chapter 2 and 
Appendix D), the surface mapping data could be interpolated along with calibration 
station and mid-channel, fixed-station depth profiles to enhance volumetric estimates 
of dissolved oxygen. Advancements in monitoring attainment technology that enable 
deployment of automated vertical profilers and surface and bottom buoy monitors 
could also support this effort. Overall, the integration of data types such as contin¬ 
uous monitoring, mapping, remote sensing, and fixed-station profiles poses one of 
the greatest challenges in criteria assessment. 

Water quality mapping cruises cannot cover every shallow-water cove and creek in 
a segment, thus presenting a problem for spatial extrapolation of the data. Criteria 
assessment using the CFD method requires the use of an interpolated/extrapolated 
surface from the entire segment and does not allow for exclusion of unsampled areas. 
Almost certainly, many of the areas outside of the sampling boundary have far 
different conditions than those measured in the shallow waters of the main segment. 
These areas represent only a small percentage of each segment, but the question 
remains whether they contain more valuable habitat than the space they occupy on a 
percentage basis. 

Annually, many of the larger fish kills in Chesapeake Bay occur in these small tidal 
creeks and embayments due to anthropogenic influences or natural conditions. Two 
months after torrential rains in June 2006, a Maryland Department of Natural 
Resources aerial photography survey of the state’s Eastern Shore tributaries revealed 
that most small embayments were still clouded by silt and algal blooms to a far 
greater extent than adjacent open waters. To assess conditions adequately in these 
shallow-water tidal creeks and embayments, a probabilistic approach may be needed 
in conjunction with current shallow-water sampling design in which representative 
small tidal creeks and embayments are sampled by the surface mapping and the 


chapter vii 


Shallow-water Monitoring and Application foi Criteria Assessment 


results become a surrogate for the percentage area that these creeks represent in a 
segment. 

A STAC-convened expert panel (described in detail in Chapter 2) has reviewed the 
interpolation of spatial data. Several standardization decisions for interpolation 
methodology will need to be made to address the panel’s recommendations for 
addressing shallow-water monitoring data (STAC 2006). 


WATER CLARITY CRITERIA ASSESSMENTS USING 
SHALLOW-WATER MONITORING DATA 

The water clarity assessment uses data from the shallow-water water quality 
mapping to obtain high-resolution data in nearshore shallow waters. This section 
describes the data analysis protocols for application of high-resolution turbidity 
measurements to assess attainment of state-adopted water clarity criteria in shallow- 
water monitored tidal tributaries and embayments of the Chesapeake Bay. 

During each day of water quality mapping with the Dataflow, the operator stops at 
five to eight locations (calibration stations) to measure photosynthetic active radia¬ 
tion (PAR) so that the light attenuation coefficient (K d ) can be calculated and 
correlated with the in situ turbidity values recorded simultaneously. The protocol 
followed to derive this correlation is described below. 

The Chesapeake Bay water clarity criteria were published as the percent of light 
through water (see Table IV-1 on page 96 in U.S. EPA 2003a). Through the applica¬ 
tion of the equation: 

PLW= 100 exp(-K d Z) Equation 3 

the appropriate percent light-through-water value and the selected water clarity 
criteria application depth (Z) are inserted and the equation is solved for K d . The 
methodology developed by the Chesapeake Bay Program for assessing criteria 
attainment involves a sequence of steps that leads to a cumulative frequency diagram 
(CFD) as described in eight steps in Table II-1 in Chapter 2. As part of step 3, 
equating the in situ collected values of turbidity to estimated K d values becomes 
necessary to determine exceedance of the water clarity criterion. It is critical to 
convert in-situ turbidity to estimates of K d prior to any data interpolation in order to 
reduce the error potential. 

The relationship between turbidity and K d , therefore, needs to be quantified to deter¬ 
mine the turbidity threshold of the applicable water clarity criteria. This 
determination narrows the scope considerably from the traditional calibration curve 
in which the estimation of K d is based on measurements for a wide range of turbidity 
concentrations. In the current application, it is only necessary to accurately estimate 
K d from in situ measurements of turbidity in the neighborhood of the exceedance of 
the water clarity criteria. 


Shallow water Monitoring and Application for Criteria Assessment 



79 


ANALYSIS ISSUES 

In conducting the analysis to formulate the decision rules and calibration curves that 
relate in situ turbidity measurements with calibration station K d measurements, 
numerous issues were addressed. Many of these issues focus on lumping or dividing 
the data when computing calibration curves. The argument in favor of lumping 
(performing the analysis on an aggregation of data) reasons that better estimates are 
obtained when averaging large numbers of observations. Lumping calibration data 
over time (e.g., one year) was assumed valid because the light-scattering properties 
of a tributary’s suspended sediments would remain relatively constant over time. 

On the other hand, the turbidity-to-K d relationship may prove inconsistent across 
different segments or entire tidal tributaries. After reviewing the Maryland and 
Virginia shallow-water monitoring data for 2003 to 2005, it was decided to divide 
the data into similar groups for individual calibration models and to conduct a cluster 
analysis for the group of tributaries monitored from 2003 to 2005. Algorithms for 
each group were developed that led to better overall precision. 

Other water quality parameters were tested for their ability to predict K d . Chloro¬ 
phyll and salinity from the calibration sites are also predictors of K d but their 
contribution is smaller than turbidity. Colored dissolved organic matter are likely to 
increase K d , however, these measurements are not routinely collected by the Chesa¬ 
peake Bay Water Quality Monitoring Program. Individual calibration curves may 
prove necessary for areas around the Bay where freshwater input from “blackwater” 
streams (e.g., the Pocomoke River) that drain extensive wetlands results in relatively 
high concentrations of colored dissolved organic matter. 

STATISTICAL MODELING 

The continuous turbidity measurements are calibrated to predicted light attenuation 
through the water column (K d ) by using statistical relationships among simultaneous 
measurements of turbidity, chlorophyll, salinity measurements, and light attenuation 
profiles of underwater photosynthetically available radiation (PAR) from five to 
eight calibration stations within each Chesapeake Bay segment. A multiple regres¬ 
sion model of K d vs. 1.5 root of turbidity [i.e., (turbidity) 1/15 ] x chlorophyll x 
salinity provides the best fit of the K d -to-turbidity relationship. The 1.5 root yielded 
the lowest root mean square prediction error and the highest r-square value. 

Figure VII-9 shows simple linear regressions of predicted K d versus the 1.5 root of 
measured turbidity for each of the seven Virginia tidal tributaries having shallow- 
water monitoring data from 2003 through 2005. Some of the slopes are similar but 
clearly different than others, indicating that data from small groups of tributaries 
with similar slopes can be combined into one calibration curve. 

The linear regression was further expanded to include terms for in situ chlorophyll 
and salinity. Like turbidity, the relationships between chlorophyll and K d , and 
salinity and K d vary among tributaries. However, enough similarities between 


chapter vii 


Shallow water Monitoring and Application for Criteria Assessment 


80 



1.5 root Turb 

system -Chickahominy — James Lynnhaven 

-Mattaponi Piankatank Pumunkey 

York 


Figure VII-9. Simple linear regression of predicted K d versus the 1.5 root of measured 
turbidity using shallow water monitoring data from seven Virginia tidal tributaries 
(2003-2005). 

Source: Virginia Institute of Marine Science—www2.vims.edu/vecos. 


coefficients and intercepts occur to form groupings of tributary data for calibration 
purposes. The groupings developed to date reflect a strong geographic pattern, which 
strengthens their validity. 

INTERPOLATION 

The very dense in situ measurements of turbidity from each sampling cruise track 
(Figure VII-10) are first converted to K d . The natural log of the converted K d values 
are then interpolated using a standardized ordinary kriging procedure with ARC/GIS 
into a 25-meter square grid over the segment’s entire surface area. Once interpo¬ 
lated, the resultant interpolated K d values are transformed back. Each interpolator 
cell within a segment’s shallow-water area is then assessed against a specific K d 
value for each applicable water clarity criterion application depth. An interpolator 
cell value equal to or below this K d value is considered in attainment of the appli¬ 
cable water clarity criterion. A number above this value has failed to meet the 
applicable water clarity criterion. 

The entire area within the shallow-water designated-use zone for each sampling 
cruise is then aggregated on an interpolator cell-by-cell basis to determine the total 
area either in attainment or failing to meet the applicable water clarity criterion. 
Water clarity attainment acres are determined for the total area within the shallow- 
water area of each segment from the shoreline out to the 2-meter depth contour 
excluding the delineated SAV no-grow zones (see Chapter 5 for details). 


chapter vii 


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81 



Figure VII-10. Example lower York River polyhaline segment YRKPH Dataflow sam¬ 
pling cruise track on August 25, 2003. 

Source: Virginia Institute of Marine Science—www2.vims.edu/vecos.. 


Water clarity criteria attainment can also be assessed in each segment’s shallow- 
water designated-use habitat through application of the CFD-based methodology 
described in Chapter 2 for each three-year assessment period. Exceedance is the 
cumulative frequency distribution of the portion of this zone that failed the K d - 
equivalent of the application depth specific water clarity criterion determined for that 
segment compared to a reference CFD curve. 

Naturally, environmental conditions will result in periodic exceedances of bay grass 
water clarity requirements; such exceedances are allowable for bay grass survival 
(U.S. EPA 2003a). Since allowable exceedances can be specific to salinity-based bay 
grass communities, biologically based reference curves are applied using measured 
water clarity exceedances established from existing bay grass beds for each salinity 
region using mid-channel water quality data (see Figures VI-1 and VI-2 in Chapter 
5). Figure VII-11 shows a preliminary example of a biological reference curve of 
water clarity exceedances based on shallow-water monitoring data for established 
bay grass beds in the polyhaline lower York River segment (YRKPH) during the 
2003 and 2004 growing seasons. This curve is plotted along with the previously 
published water clarity reference curve for mesohaline/polyhaline shallow-water bay 
grass designated-use habitats (U.S. EPA 2003a). 


chapter vii 


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82 



Percent of Area Exceeding Criteria 


Figure VIM 1. Comparison of the published mesohaline/polyhaline water clarity criteria 
biological reference curve based on mid-channel water clarity measurements and a 
preliminary example of a shallow-water monitoring-based water clarity criteria biological 
reference curve. 

Source: : U.S. EPA 2003a; Virginia Institute of Marine Science—www2.vims.edu/vecos. 


CHLOROPHYLL A CRITERIA ASSESSMENTS USING 
SHALLOW-WATER MONITORING DATA 

Attainment of the chlorophyll a criteria in the shallow-water designated use areas 
will be based upon the adjacent open-water designated use assessments. As with 
dissolved oxygen assessments, open-water chlorophyll a assessments will rely on 
the mid-channel fixed station data combined with Dataflow and Dataflow calibration 
profile data. These data will be interpolated and analyzed, along with the fixed- 
station chlorophyll a data, using the Chesapeake Bay Program’s interpolator and 
CFD approach described in Chapter 2. The following sections describe the rationale 
for and development of protocols for using the in-situ fluorescence measurements 
from the Dataflow system to assess chlorophyll a criteria attainment in shallow and 
open-water tidal tributaries and embayments of Chesapeake Bay. 

The Dataflow system generates a data set that better represents the spatial variability 
of chlorophyll. The Dataflow cruise track transverses both the open and shallow 
water designated use areas (see Figure VII-10), recording hundreds of fluorescent 
measurements, very quickly and less expensively than the collection and laboratory 
analysis of individual samples. However, the conversion of the fluorescence data to 
chlorophyll a must be done carefully to ensure that they are comparable to the 
chlorophyll o data upon which the chlorophyll a criteria were based. 


chapter vii 


Shallow water Monitoring and Application for Criteria Assessment 




























83 


The in-situ fluorescence method is more susceptible to bias and interferences than 
the laboratory method. Instrument manufacturers recognize that low temperatures 
and high turbidities can affect the fluorescence response and note that different 
phytoplankton species can fluoresce differently in-situ even if the actual chlorophyll 
content is the same (YSI, Inc. 1999). To overcome these effects, it is a common prac¬ 
tice to “calibrate” the in-situ data to the laboratory results by collecting and 
analyzing a set of chlorophyll a samples in the laboratory concurrent with in-situ 
measurements, and establishing a quantitative relationship, or “calibration” between 
the methods via simple linear regression. The calibration may be done for each day 
of sampling but better estimates may result if greater numbers of observations are 
incorporated into a statistical model. 

STATISTICAL MODELING 

The usual approach for calibrating in situ fluorescence to in vitro chlorophyll is to 
develop a model of the form: 

Chlorophyll = f(fluorescence, other variables). Equation 4 

Usually the function f is a linear regression model and the estimates of the coeffi¬ 
cients for this model are obtained using least squares. With this model, a measured 
value of fluorescence may be used as an argument to obtain a predicted chlorophyll 
value. By evaluating other water quality variables measured by the monitoring 
program, it was determined that fluorescence, temperature, turbidity, pH, and 
seasonal variables be used as independent variables as described above. 

One problem with this standard approach is that least squares estimation requires 
that data used as independent variables be measured without error. Clearly this 
assumption is not satisfied for fluorescence. An alternative approach that treats both 
in vitro and in situ chlorophyll as variables with measurement error estimates the 
logarithm of their ratio with a linear regression model: 

Log (R) = LogCChL / Chl 2 ) = f(other variables) Equation 5 

where: 

Chi) = in vitro chlorophyll 

Chl 2 = in situ chlorophyll (note: fluorometers used to collect data for this 
study convert the fluorescence signal to chlorophyll with a standard 
algorithm and this is the number recorded); and 

R = the ratio of these two chlorophyll measures. 

An estimate of in vitro chlorophyll is obtained from the in situ measurement by first 
estimating the logarithm of R given the independent variables, back-transforming to 
obtain an estimate of the ratio, and multiplying the in situ chlorophyll by the ratio to 
estimate the in vitro chlorophyll. 


chapter vii 


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84 


MODELING APPROACH 

Continuous monitoring data for Maryland and Virginia were analyzed to determine 
a method of post-calibrating fluorescence/chlorophyll to match extractive chloro¬ 
phyll more precisely. Because the instruments are identical, it was assumed that the 
relationships between the Dataflow fluorescence and chlorophyll a would show 
similar patterns. Maryland data were available for 2003 through 2005 for approxi¬ 
mately 21 tidal tributaries (not all tributaries were sampled in all three years). 
Virginia data came from the York River. Initial tests indicated that no more variation 
occurred between Maryland and Virginia data than among the tidal tributaries in 
Maryland. This finding simplified the post-calibration model geographically by 
allowing combination of data from both states. 

A second test of the data evaluated potential differences among years. This test also 
proved negative, which signified that all three years of data could be combined when 
developing the post-calibration model. Tests of season and tributary differences 
suggested that the final model would need to account for temporal and spatial differ¬ 
ences. Further analyses indicated the need for two tributary groups and two season 
groups, meaning that four calibration curves will be required. Significant variables 
in the model also included water temperature, turbidity, and pH. Significance is 
defined here as a p-value of less than 0.05. 

Initial results indicate that four calibration curves would be needed, two for season 
and two for tributary. All four models contain fluorescence, water temperature, 
turbidity, and pH. 

ANALYSIS ISSUES 

Several issues were addressed in conducting the analysis to formulate the decision 
rules and calibration curves. Similar to the turbidity/K d relationship, many of the 
issues related directly to the decision to lump or divide the data when computing 
calibration curves and decision rules. The argument in favor of lumping (to perform 
the analysis on a data aggregate) reasons that better estimates result when large 
numbers of observations are averaged. On the other hand, the in situ to in vitro rela¬ 
tionship may not be consistent across all subsets of the data (i.e., between different 
tidal tributaries and embayments). If so, dividing the data and developing algorithms 
for each set may lead to better overall precision. 

Seasonal Patterns 

Because species composition can affect the relationship of in situ to in vitro chloro¬ 
phyll measurements, this relationship may change with the seasons. Thus, one 
aggregate-or-divide issue requiring resolution is the effect of seasons. 

The in situ/in vitro difference generally follows a seasonal pattern consistent with 
known species composition patterns for Chesapeake Bay and its tidal tributaries. In 
situ chlorophyll measurements have a negative bias when phytoplankton populations 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 


85 


shift toward a large component of blue-green algae. Blue-green algae increase in 
abundance during mid to late summer, particularly in tidal-fresh to low-salinity habi¬ 
tats. The calibration data from both the continuous monitors and the Dataflow water 
quality mapping show that the negative bias of the in situ measure becomes greater 
in summer. It was determined that two season groups would be needed. 

It must be recognized that forming two season groups implements a model that 
captures the average condition, but may not capture the condition that exists in a 
particular tributary on a given date. The seasonal appearance of blue-green algae is 
not the same across tributaries and not even the same within a tributary from year to 
year. Even if the model predictions agree well with the observed data for the past 
three years, it is quite possible that a blue-green bloom could form at some unusual 
time of year in the future and lead to biased prediction. Truly reliable calibration of 
in situ chlorophyll to in vitro chlorophyll requires that some information on the 
concentration of blue-green cells be included in the calibration model. 

Geographic Patterns 

Geography is another general factor that may influence the in situ to in vitro chloro¬ 
phyll a relationship. Again, this influence is likely to be a phytoplankton species 
composition effect. Other factors (e.g., turbidity), however, may play a role. It is 
recommended that the analysis model the geography by treating locations (fixed- 
stations for continuous monitors or river systems for Dataflow) as discrete 
categorical predictors. If these predictors are statistically significant, the geography 
portion of the model should be simplified using surrogate variables, such as salinity 
and turbidity. 

Spatial patterns emerge with data set analysis. These patterns, when viewed 
geographically, appear to follow arrangements expected based on phytoplankton 
species composition. In the Virginia Dataflow data, the trend is longitudinal within 
the estuaries. In the tidal-fresh region, the in situ and in vitro measurements appear 
similar, with a negative bias of in situ relative to in vitro emerging in downstream 
stations (Figure VII-12). In the upper tidal Mattaponi River, one region occurs in 
which in situ has a positive bias relative to in vitro. This situation may occur due to 
high background fluorescence from tannins (dissolved organic carbon) in the water. 
In Maryland, the negative bias (yellow squares) appears in regions where blue-green 
populations have been identified; however, the data do not show a longitudinal 
gradient similar to the Virginia data (Figure VII-13). 

Diet Patterns 

In continuous monitoring data, many locations exhibit distinct diel patterns in the in 
situ chlorophyll. This diel pattern often shows that chlorophyll is higher at night and 
lower during the day. Other research has shown that fluorometric chlorophyll read¬ 
ings made in direct sunlight will be biased low because sunlight inhibits 
phytoplankton fluorescence. This finding, coupled with the observed pattern of lower 
in situ chlorophyll during the day, raised the concern that continuous monitoring of 


chapter vii 


Shallow-water Monitoring and Application tor Criteria Assessment 


86 



Figure VII-12. Locations of the Virginia Chesapeake Bay Shallow- 
water Monitoring Program calibration stations. In each location, a 
circle indicates that no significant difference occurs between the in 
situ chlorophyll measures and the in vitro chlorophyll measures. A 
square indicates that the in situ measures are less than the in vitro 
measures. An X indicates that in-situ measures are greater than the 
in-vitro measures. 

Source: Virginia Institute of Marine Science—www2.vims.edu/vecos. 


in situ chlorophyll might be biased low during the day because of this measurement 
problem. A special study was conducted at the Jug Bay station on the tidal Patuxent 
River collecting hourly calibration samples for 24 hours. One set of samples was 
collected monthly from March to December in 2005. Analysis of the in situ/in vitro 
difference shows a very slight diel pattern in these data, but this variability became 
trivial when compared to other sources of variance. 

Collection Agency 

The two principal agencies collecting these data—the Maryland Department of 
Natural Resources and the Virginia Institute of Marine Sciences—have devoted 
considerable effort to maintaining comparable shallow-water monitoring program 
field collection methodologies, instrumentation, and QA/QC procedures. Even so, 
because the two agencies work in geographically distinct regions, comparing results 
between agencies to determine if these data can be combined to estimate calibration 
curves should prove useful. Initial data evaluations indicate that no more variation 
exists between Maryland and Virginia data than among the tidal tributaries in Mary¬ 
land. These evaluations suggest that any differences between Maryland and Virginia 
data may actually result from variations among the tidal tributaries and not from 
dissimilarities between the data-collecting agencies. 

chapter vii • Shallow-water Monitoring and Application for Criteria Assessment 













87 



Figure VIM3. Locations of the Maryland Chesapeake Bay Shallow-water 
Monitoring Program continuous monitors. In each location, a circle indicates 
that no significant difference exists between the in situ chlorophyll measures 
and those for in situ chlorophyll. A square indicates that the in situ measures 
are less than the in vitro measures. 

Source: Department of Natural Resources-www2.eyesonthebay.net. 


Background Fluorescence 

In some Bay areas, the background fluorescence constitutes a significant component 
of the total fluorescence signal due to freshwater input from blackwater streams. 

Background fluorescence is the fluorescence measured on filtered water. This study 
will identify those areas where background fluorescence requires measurement and 
develop an algorithm to adjust for background fluorescence. Analysis indicates that 
background fluorescence is not significant in the systems assessed to date. 

Ancillary Data 

While conventional wisdom holds that in vitro methods produce more accurate 
measures of chlorophyll than in situ methods, both are still subject to error. Using 
data collected independently of either type, the relative accuracy of the two method¬ 
ologies will be assessed. For example, measurements taken as part of the nutrient 
suite (e.g., particulate nitrogen, total nitrogen, etc.) have some predictive power for 
chlorophyll. In cases where the in situ and in vitro measurements differ by more than 
expected due to sampling error, these ancillary data may resolve which is more reliable. 

Often a time series of both in situ and in vitro chlorophyll will show that the two 
measurements compare quite well for much of the data record, with occasional large 
discrepancies. Because these large discrepancies are most problematic from a 

chapter vii • Shallow-water Monitoring and Application for Criteria Assessment 















88 


decision-rule point of view, they warrant special consideration. If one of the methods 
is more likely to be in error when these discrepancies occur, this finding will affect 
use of that method in the regulatory process. 

To address this issue, separate models of in situ chlorophyll and in vitro chlorophyll 
need to be developed for which the independent variables are taken from the suite of 
nutrient measurements (e.g., total nitrogen, particulate nitrogen, etc.). A pilot project has 
shown that these models are fairly predictive. In a case where a large discrepancy 
between the in situ and the in vitro measurements exists, if one is in agreement with its 
predictive model and the other is not, then the one out of agreement is likely in error. 


LITERATURE CITED 

Batiuk, R.A., P. Bergstrom, M. Kemp, E. Koch, L. Murray, J.C. Stevenson, R. Bartleson, V. 
Carter, N.B. Rybicki, J.M. Landwehr, C. Gallegos, L. Karrh, M. Naylor, D. Wilcox, K.A. 
Moore, S. Ailslock, and M. Teichberg. 2000. Chesapeake Bay Submerged Aquatic Vegetation 
Water Quality and Habitat-Based Requirements and Restoration Targets: A Second Technical 
Synthesis. CBP/TR 245/00 EPA 903-R-00-014. U.S. EPA Chesapeake Bay Program, 
Annapolis, MD. 

Chesapeake Executive Council. 2000. Chesapeake 2000. Chesapeake Bay Program, 
Annapolis, MD. 

Karrh, L. 1999. Comparison of Nearshore and Midchannel Water Quality Conditions. 200 
pp. Chesapeake Bay Program, Annapolis, MD. 

Maryland Department of Natural Resources. 2006. Quality Assurance Project Plan for the 
Maryland Department of Natural Resources Chesapeake Bay Shallow Water Quality Moni¬ 
toring Program for the period of July 1, 2006 - June 30, 2007. Maryland Department of 
Natural Resources, Annapolis, MD. 

Scientific and Technical Advisory Committee (STAC). 2005. Final Report of the Chesapeake 
Bay Scientific and Technical Advisory Committee Workshop: Evaluating the Design and 
Implementation of the Chesapeake Bay Shallow Water Monitoring Program Chesapeake Bay 
Program Scientific and Technical Advisory Committee Publication 05-003. 

Scientific and Technical Advisory Committee (STAC). 2006. The Cumulative Frequency 
Diagram Method for Determining Water Quality Attainment: Report of the Chesapeake Bay 
Program STAC Panel to Review Chesapeake Bay Analytical Tools. STAC Publication 06-003. 
9 October 2006. Chesapeake Bay Program Scientific and Technical Advisory Committee. 
Chesapeake Research Consortium, Edgewater, MD. 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

Virginia Institute of Marine Science. 2005. Quality Assurance Project Plan for Shallow 
Water Monitoring. Virginia Institute of Marine Science, College of William and Mary, 
Gloucester Point, VA. 

YSI, Inc. 1999. Environmental Monitoring Systems, 6-Series Operations Manual. 


chapter vii 


Shallow-water Monitoring and Application for Criteria Assessment 



89 


cha pter \/Ii8 

Framework for Chesapeake Bay 
Tidal Waters 303(d) List 
Decision-Making 


BACKGROUND 

Section 303(d) of the Clean Water Act and EPA Regulation 40CFR 130.7 requires 
biennial identification of water segments that are not attaining water quality stan¬ 
dards. These segments must have a total maximum daily load (TMDL) analysis 
completed and allocations established that result in water quality standards attain¬ 
ment. The states comply with this requirement through a process known as the 
Integrated Reporting Requirements which covers the assessment and listing require¬ 
ments through Clean Water Act sections 305(d), 305(b), and 314 (U.S. EPA 2005b). 

Given that the 2006 integrated reporting documents would be the first prepared 
under the states’ newly adopted Chesapeake Bay water quality standards regulations, 
a collaborative effort (among the EPA and watershed states) began in spring 2005 to 
develop a decision-making framework for that portion of the 2006 submittals 
addressing the Chesapeake Bay system. The Chesapeake Bay Program partners 
reached agreement on several key assessment and listing issues. This chapter docu¬ 
ments these agreements and presents the resultant flowchart for Chesapeake Bay 
tidal-water listing decisions to guide Delaware, Maryland, Virginia, and the District 
of Columbia in future 303(d) listing cycles. 


LISTING CATEGORY DECISIONS 

Each state-adopted, tidal-water designated use by Chesapeake Bay Program segment 
(or formally adopted state sub-segment) is considered an individual spatial assess¬ 
ment unit for the purposes of each state’s 303(d) list (U.S. EPA 2003a, 2003b, 2004a, 
2004b, 2005a). 

If a segment has been previously listed in category 5—recognizing the recent adop¬ 
tion of new Chesapeake Bay water quality standards—the original listing decision 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision-Making 




90 


should stand until sufficient data are available to fully assess attainment for all appli¬ 
cable criteria components in each designated-use segment assessment unit. With 
sulficient data, states can justify moving an individual designated-use segment, or 
the segment as a whole, to another listing category. The lack of sufficient data for 
full assessment of the applicable criteria is not justification for moving a category 5 
(impaired) segment to category 3 (insufficient data). 

It a segment’s designated use was not previously listed in category 5, it can be listed 
under category 3 if insufficient data exist to assess attainment of all applicable 
criteria components. Because an individual segment may have up to five tidal-water 
designated uses (see Table V-l in Chapter 5), the states can place individual 
segments in multiple listing categories based on the criteria assessment results for 
each designated use in the segment. 

CRITERIA ATTAINMENT ASSESSMENTS 

The preceding chapters document the different Chesapeake Bay water quality 
criteria assessments. Across all Bay criteria, non-attainment is defined as any 
percentage of non-attainment (even less than 1 percent) given that the CFD-based 
criteria attainment assessment method already factors in the small percentage of 
circumstances (in time and space) in which the criteria may be exceeded and still 
fully protect the tidal-water designated use (U.S. EPA 2003a). 

DISSOLVED OXYGEN CRITERIA ATTAINMENT ASSESSMENT 

Given that multiple criteria often protect an individual designated use (e.g., separate 
30-day mean, 7-day mean, and instantaneous minimum criteria required for protec¬ 
tion of the open-water fish and shellfish designated use), full attainment of the 
dissolved oxygen criteria must involve assessment of each applicable criterion indi¬ 
vidually (U.S. EPA 2003a). In designated-use-segment assessment units for which 
data are available to assess all applicable dissolved oxygen criteria, the states can 
proceed with a full assessment of attainment of that segment’s designated use. For 
those units with insufficient data for one or more of these criteria, states should not 
make any decisions on removing that designated-use segment from part 5 during that 
listing cycle. 

Until the EPA publishes methodologies for assessing the 7-day and 1-day mean, 
along with the instantaneous minimum open-water and deep-water dissolved oxygen 
criteria components, the EPA recommends the states rely strictly on the assessment 
of the 30-day mean open-water and deep-water dissolved oxygen criteria for listing 
decisions. For those open- and deep-water designated-use segments for which the 
30-day mean criteria are in non-attainment, the jurisdictions should list the segment 
on part 5 as impaired in the absence of data or methodologies for assessing the 
remaining criteria components. For those designated-use segments in which the 30- 
day mean open- or deep-water criteria are in attainment, the jurisdictions should 
generate additional data and apply criteria assessment procedures to determine 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 


91 


attainment of the 7- and 1-day means as well as the instantaneous minimum criteria 
components. If a segment was first listed in 2006 based on the 30-day mean open- 
water and/or deep-water criteria and subsequent 30-day mean open-water and/or 
deep-water criteria data now shows the segment to be in attainment, then the segment 
may be delisted for these criteria. 

WATER CLARITY CRITERIA ATTAINMENT ASSESSMENT 

The shallow-water bay grass designated use is in attainment if a sufficient number of 
acres of SAV occur within the segment or if enough acres of shallow-water habitat 
exist that meet the applicable water clarity criteria to support restoration of the 
desired acreage of SAV for that segment (U.S. EPA 2003a, 2003b). Assessment of 
either measure, or a combination of both, can serve as the basis for determining 
attainment or impairment of the shallow-water bay grass designated use. 

Since SAV is the ultimate biological measure of attainment of the designated use, in 
the absence of sufficient shallow-water monitoring data necessary to determine the 
available water clarity acres or assess water clarity criteria attainment using the 
CFD-based criteria assessment procedure, EPA recommends the States assess 
shallow-water bay grass designated use attainment/impairment based on the acres of 
mapped SAV. 

If a shallow-water bay grass designated-use segment meets its SAV restoration 
acreage, that designated use-segment is in attainment of the designated use and 
should be listed on part 2. 

If such a segment does not meet its restoration acreage, the jurisdiction can then 
assess attainment using water clarity acres or water clarity criteria as described in 
Chapter 5. If the water clarity acres or water clarity criteria are attained based on 
shallow-water monitoring data, then that segment is in attainment of the shallow- 
water bay grasses designated use and should be listed on part 2. 

Finally, if the water clarity restoration acres or water clarity criteria are not attained 
using the same data, or if there are insufficient data to make a determination using 
water clarity acres or water clarity criteria, then that segment is not in attainment of 
the shallow-water bay grasses designated use and should be listed on part 5. 

Any attainment/non-attainment determination of water clarity criteria based on mid¬ 
channel-based monitoring is strictly diagnostic. These mid-channel data should not 
directly form the basis for any listing decision based on attainment/non-attainment 
of a segment’s shallow-water bay grass designated use. 

CHLOROPHYLL A CRITERIA ATTAINMENT ASSESSMENT 

As described in Chapter 6, numerical chlorophyll a criteria attainment is assessed by 
applying the appropriate numerical criteria over the applicable season for three years 
using the CFD-based criteria assessment methodology. 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision-Making 


92 


BENTHIC INDEX OF BIOTIC INTEGRITY ASSESSMENT 

The benthic community health assessment is conducted in three phases to support 
the states’ tidal waters listing decisions (Llanso et al. 2005) (Appendices J and K). 
Phase I evaluates the sample size from the segment during the five-year assessment 
window. An impairment assessment based on benthic community health is not 
possible if the sample size requirement is not met. The data, however, may still prove 
useful as an adjunct to other aquatic life use data. If the sample size satisfies the 
requirements of the statistical method (n > 10), a formal assessment of status (i.e., 
impaired vs. supports aquatic life use) is determined using the “percent degraded 
area” statistical methodology (Phase II). 

Phase II assesses aquatic life use impairment based on a comparison of the Chesa¬ 
peake Bay benthic index of biotic integrity or benthic-IBI scores (Weisberg et al. 
1997). This assessment is possible only when the number of benthic-IBI scores 
within a segment is sufficient to meet the sample size requirement of the approved 
statistical method (n > 10). Phase II can result in one of two possible outcomes: 1) 
the segment is not impaired for aquatic life use due to benthic community status 
(note that the segment may still be impaired due to failure of the other aquatic life 
use subcategories or criteria); or 2) the segment fails to support aquatic life use due 
to benthic community status and is assessed as impaired (part 5). 

Phase III identifies the probable causes of assessed benthic impairment of the 
segment using a diagnostic tool that can pinpoint potential sources of stress affecting 
benthic community conditions in the Chesapeake Bay (Dauer et al. 2005). This 
methodology can also identify causes of stress and quantify the magnitude of degra¬ 
dation. In addition, it distinguishes stress due to contaminants from stress due to 
other factors (Appendix L). 

ASSESSMENT REPORTING FRAMEWORK 

A Chesapeake Bay tidal-water designated-use criteria attainment assessment spread¬ 
sheet has been developed to assist the states in reporting listing decisions for each 
designated-use segment (Table VIII-1). The assessment reporting framework effi¬ 
ciently documents relevant information as each segment goes through the listing 
decision flowchart described below. 

Table VIII-2 shows the example results of the Chesapeake Bay benthic analysis for 
the 2006 303(d) reporting cycle. The benthic-IBI assessments are separate from the 
Chesapeake Bay water quality criteria attainment assessment determinations and 
reported for the segments as stand-alone or supplemental information for the states 
to use in their 303(d) listing cycle decisions. 


chapter viii 


Framework (or Chesapeake Bay Tidal Waters 303(d) List Decision Making 


Table VIII-1. Chesapeake Bay tidal-water designated-use criteria attainment assessment reporting format 


93 


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chapter viii * Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 







































































Table VI11-2. Examples of the array of results from the states' 2006 listing cycle evaluation of benthic community health. 


94 


Suspected Sources of 

Benthic Community 

Degradation 

Sediment Contaminants 

Sediment Contaminants 

r • .. 

Unknown 

Low DO 

Low DO 

Unknown 

Low DO 

Unknown 

Eutrophication 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Unknown 

Degraded Samples with 
Insufficient 

Abundance/Biomass; % 
of Total w/o Cont. 

o 

o 

OC 

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tO 

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42.86 

10.34 

27.27 

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20 

13.33 

— 

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13.95 

10.42 

Degraded Samples 
with excessive 
Abundance/Biomass; 

% of Total w/o Cont. 

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Samples with 
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p>= 0.90; 

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17.65 

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7.14 

KOI 

909 

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o 

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10.53 

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— 

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43 

48 

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CB7PHa 

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chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 


NA= Insufficient sample size for impairment decision. 
* See Table 3 in Llanso et al. 2005 






































95 


LISTING DECISION FRAMEWORK 

The Chesapeake Bay Program partners reached agreement on how to apply the 
results of the refined criteria assessment procedures most effectively in making 
Chesapeake Bay tidal-water listing decisions. The resultant decision-making frame¬ 
work, presented here in the form of a flowchart, can guide jurisdictional decisions in 
preparing future integrated reporting cycle submissions for the Chesapeake Bay 
system (Figure VIII-1). 

All of the designated-use segment combinations for the five possible tidal-water 
designated uses—migratory fish spawning and nursery, shallow-water bay grass, 
open-water fish and shellfish, deep-water seasonal fish, and shellfish and deep- 



'Dissolved oxygen, water clarity and chlorophyll u. 
: SAV acreage and benthic index of biotic integrity. 


Figure VIII-1. The 303(d) listing decision flow chart for assessing tidal waters designated 
uses in Chesapeake Bay and tidal tributaries. 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 































































96 


channel seasonal refuge—along with the relevant dissolved oxygen, water 
clarity/SAV restoration acreage, and chlorophyll a criteria are applied through this 
listing decision flowchart. Benthic index of biotic integrity data are also evaluated 
for listing decisions. 

The listing decision flowchart starts with each designated use-segment-applicable 
criterion combination, asking whether that segment was previously listed in category 
5 as impaired based on the specific water quality (dissolved oxygen, water clarity, 
chlorophyll a) or biological (SAV acreage) criterion parameter. If yes, its initial 
listing status remains in category 5 pending new criteria attainment assessments. If 
no, then the flowchart questions whether data now exist to assess criteria attainment. 

SEGMENTS PREVIOUSLY LISTED AS IMPAIRED 

At the second level, the flowchart queries whether the available data are sufficient to 
reassess criteria or index attainment. If yes, the third level asks if the applicable 
criteria or index is attained. If all applicable criteria components and indices have 
been attained, the designated-use segment is then listed in part 2. If no, the desig- 
nated-use segment remains in part 5. If insufficient data exist at the second level to 
assess criteria attainment/index attainment, the designated-use segment previously 
listed as impaired remains in part 5. 

SEGMENTS NOT PREVIOUSLY LISTED AS IMPAIRED 

At the second level, the flowchart queries whether the available data are sufficient 
to reassess criteria or index attainment. If yes, the third level determines whether 
the applicable criteria or index is attained. If all applicable criteria components and 
indices have been attained, the designated-use segment is then listed in part 2. If 
no, the designated-use segment is listed as impaired in part 5. If insufficient data 
exist at the second level to assess criteria attainment/index attainment, the desig¬ 
nated-use segment remains in part 3. 

SHALLOW-WATER DESIGNATED-USE LISTING DECISIONS 

If a shallow-water designated-use segment does not meet its SAV restoration 
acreage, the EPA recommends that the state list this designated-use segment in cate¬ 
gory 5 presuming the shallow-water monitoring data needed to assess water clarity 
acres/criteria attainment do not exist. 


LITERATURE CITED 

Dauer, D.M., M.F. Lane, and R.J. Llanso. 2005. Addendum to the Report: Development of 
Diagnostic Approaches to Determine Sources of Anthropogenic Stress Affecting Benthic 
Community Condition in the Chesapeake Bay. Prepared for U.S. Environmental Protection 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 



97 


Agency. Chesapeake Bay Program Office, by Department of Biological Sciences, Old 
Dominion University. Norfolk. VA, and Vcrsar, Inc., Columbia, MD. 

Llanso, R.J., J.H. Vplstad. D.M. Dauer. and M.F. Lane. 2005. 2006 303(D) Assessment 
Methods For Chesapeake Bay Benthos. Final report submitted to Virginia Department of 
Environmental Quality, September 2005. Versar Inc., Columbia, MD, and Old Dominion 
University, Norfolk, VA. 

U.S. Environmental Protection Agency (U.S. EPA). 2003a. Ambient Water Quality Criteria 
for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal 
Tributaries. EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, 
MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. EPA 903-R-04-008. 
CBP/TRS 268/04. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability: 2004 Addendum. EPA 903-R-04-006. Region III 
Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2005a. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003: 2005 Addendum. 
EPA 903-R-05-004. CBP/TRS 278-06. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2005b. Guidance for 2006 Assessment, Listing and 
Reporting Requirements Pursuant to Sections 303(d), 305(b) and 314 of the Clean Water Act. 
July 29, 2005. Office of Water. Office of Wetlands. Oceans and Watersheds, Assessment and 
Watershed Protection Division. Washington, D.C. 

Weisbcrg, S B., J.A. Ranasinghe, D.M. Dauer, L.C. Schaffner, R.J. Diaz, and J.B. Frithscn. 
1997. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries 
20: 149-158. 


chapter viii 


Framework for Chesapeake Bay Tidal Waters 303(d) List Decision Making 


98 


Acronyms 


Id 13 

inverse of the distance 
squared 

mg chla m" 

milligrams of chlorophyll a 
per meter squared 

°c 

degrees Celsius 

mg liter 1 

milligrams per liter 

CART 

classification and 
regression tree 

NASS 

non-algal suspended solids 

CBP 

Chesapeake Bay Program 

nh 4 

ammonium 

CDOM 

colored dissolved organic 
matter 

no 2 

nitrite 

CFD 

cumulative frequency diagram 

no 3 

nitrate 

cells/ml 

cells per milliliter 

o 2 

oxygen 

Chla 

chlorophyll a 

PAR 

photosynthetically active 
radiation 

DIN 

dissolved inorganic nitrogen 

P0 4 

dissolved inorganic 

phosphorous/ 

orthophosphorous 

DO 

dissolved oxygen 

ppt 

parts per thousand 

g C m “ d 

1 grams of carbon per meter 
squared per day 

PSU 

practical salinity unit 

GLM 

general linear model 

QA/QC 

quality assurance/quality 
control 

HAB 

harmful algal bloom 

SAV 

submerged aquatic 
vegetation 

IDW 

inverse-distance weighting 

STAC 

Science and Technical 
Advisory Committee 

kg m J m 

kilograms per cubic meter 
per meter 

TMDL 

total maximum daily load 

km 

kilometers 

TSS 

total suspended solids 

LOAEL 

lowest observable acute 
effects level 

U.S. EPA 

United States 

Environmental 

Protection Agency 

m 

meter 

A g/kg 

micrograms per kilogram 


cubic meters per second 

/ug liter -1 

micrograms per liter 

mg 

milligram 

% saturation 

percent oxygen saturation 


Acronyms 























A-1 


appendix ^31 


The Cumulative Frequency 
Diagram Method for 
Determining Water Quality 

Attainment 


Report of the Chesapeake Bay Program STAC Panel to 
Review of Chesapeake Bay Program Analytical Tools 

STAC Publication 06-003 
9 October 2006 


Panel Members: 

David Secor, Chair (Chesapeake Biological Laboratory, University of Maryland 
Center for Environmental Science) 

Mary Christman (Dept, of Statistics, University of Florida) 

Frank Curriero (Departments of Environmental Health Sciences and Biostatistics, 
Johns Hopkins Bloomberg School of Public Health) 

David Jasinski (University of Maryland Center for Environmental Science) 

Elgin Perry (statistics consultant) 

Steven Preston (US Geological Survey, Annapolis) 

Ken Reckhow (Dept. Environmental Sciences & Policy Nicholas School of the 
Environment and Earth Sciences, Duke University) 

Mark Trice (Maryland Department of Natural Resources) 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-2 


EXECUTIVE SUMMARY 

BACKGROUND AND ISSUES 

In accordance with the Chesapeake 2000 Agreement, the Chesapeake Bay Program 
has recently implemented important modifications to (1) ambient water quality 
criteria for living resources and, (2) the procedures to determine attainment of those 
criteria. A novel statistical tool for attainment, termed the Cumulative Frequency 
Diagram (CFD) approach, was developed as a substantial revision of previous attain¬ 
ment procedures, which relied upon a simple statistical summary of observed 
samples. The approach was viewed as advantageous in its capacity to represent 
degrees of attainment in both time and space. In particular, it was recognized that the 
CFD could represent spatial data in a synoptic way: data that is extensively collected 
across diverse platforms by the Chesapeake Bay Program Water Quality Monitoring 
Program. Because the CFD approach is new to Bay Program applications, under¬ 
lying statistical properties need to be fully established. Such properties are critical if 
the CFD approach is to be used to rigorously define regional attainments in the 
Chesapeake Bay. 

In Fall 2005, the Chesapeake Bay Program Scientific, Technical and Advisory 
Committee charged our working group to provide review and recommendations on 
the CFD attainment approach. As terms of reference we used guidelines of Best 
Available Science recently published by the American Fisheries Society and the 
Estuarine Research Federation. Statistical issues that we reviewed included, 

1. What are the specific analytical/statistical steps entailed in constructing CFD 
attainment curves and how are CFDs currently implemented? (Section 2) 

2. How rigorous is the spatial interpolation process that feeds into the CFD 
approach? Would alternative spatial modeling procedures (e.g., kriging) 
substantially improve estimation of water quality attainment? (Section 3) 

3. What are the specific analytical/statistical steps entailed in constructing CFD 
reference curves? (Section 4) 

4. What are the statistical properties of CFD curves? How does sampling density, 
levels of attainment, and spatial covariance affect the shape of CFD curves? 
What procedures are reliable for estimating error bounds for CFD curves? 
(Section 5) 

5. From a statistical viewpoint, does the CFD approach qualify as best available 
science? (Section 6) 

6. What are the most important remaining issues and what course of directed 
research will lead to a more statistically rigorous CFD approach over the next 
three years? (Section 7) 

The central element of our work was a series of exercises on simulated datasets 
undertaken by Dr. Perry to better evaluate 1) sample densities in time and space, 2) 
varying levels of attainment, and 3) varying degrees of spatial and temporal 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 



A-3 


covariance. Further, trials of spatial modeling on fixed station Chesapeake Bay water 
quality data by Dr.s Christman and Curriero were conducted to begin to evaluate 
spatial modeling procedures. These exercises, literature review and discussions 
leading to consensus opinion are the basis of our findings. In August 2006, the 
working group supplied preliminary findings and related text for use in the 2006 
CBP Addendum to Ambient Water Quality Criteria that is now under review. 

FINDINGS 

1. The CFD approach is feasible and efficient in representing water quality 
attainment. 

The CFD approach can effectively represent the spatial and temporal dimensions 
of water quality data to support inferences on whether regions within the Chesa¬ 
peake Bay attain or exceed water quality standards. The CFD approach is 
innovative but could support general application in water quality attainment 
assessments in the Chesapeake Bay and elsewhere. The CFD approach meshes 
well within the Chesapeake Bay Program’s monitoring and assessment 
approaches, which have important conceptual underpinnings (e.g., segments 
defined by designated uses). 

In accepting the CFD as the best available approach for using time-space data, the 
panel contrasted it with the previous method and those sustained by other juris¬ 
dictions. The previous method used by the Chesapeake Bay Program, similar to 
the approaches used in other states, was simply based on EPA assessment guid¬ 
ance in which all samples in a given spatial area were compiled and attainment 
was assumed as long as > 10% of the samples did not exceed the standard. In this 
past approach all samples were assumed to be fully representative of the specified 
space and time and were simply combined as if they were random samples from 
a uniform population. This approach was necessary at the time because the tech¬ 
nology was not available for a more rigorous approach. But it neglected spatial 
and temporal patterns that are known to exist in the standards measures. The CFD 
approach was designed to better characterize those spatial and temporal patterns 
and weight samples according to the amount of space or time that they actually 
represent. 


2. CFD curves are influenced by sampling density and spatial and temporal 
covariance. These effects merit additional research. Conditional simulation 
offers a productive means to further discover underlying statistical proper¬ 
ties and to construct confidence bounds on CFD curves, but further directed 
analyses are needed to test the feasibility of this modeling approach. 

The panel finds that the CFD approach in its current form is feasible, but that 
additional research is needed to further refine and strengthen it as a statistical tool. 
The CFD builds on important statistical theory related to the cumulative distribu¬ 
tion function and as such, its statistical properties can be simulated and deduced. 
Through conditional simulation exercises, we have also shown that it is feasible 
to construct confidence ellipses that support inferences related to threshold curves 


appendix a 


Fhe Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-4 


or other tests of spatial and temporal compliance. Work remains to be done in 
understanding fundamental properties of how the CFD represents likely covari¬ 
ances of attainment in time and space and how temporal and spatial correlations 
interact with sample size effects. Further, more work is needed in analyzing biases 
across different types of designated use segments. The panel expects that a two- 
three year time frame of directed research and development will be required to 
identify and measure these sources of bias and imprecision in support of attain¬ 
ment determinations. 


3. The success of the CFD-based assessment will be dependent upon decision 
rules related to CFD reference curves. For valid comparisons, both reference 
and attainment CFDs should be underlain by similar sampling densities and 
spatial covariance structures. 

CFD reference curves represent desired segment-designated use water quality 
outcomes and reflect sources of acceptable natural variability. The reference and 
attainment curves follow the same general approach in derivation: water quality 
data collection, spatial interpolation, comparison to biologically-based water 
quality criteria, and combination of space-time attainment data through a CFD. 
Therefore, the biological reference curve allows for implementation of threshold 
uncertainty as long as the reference curve is sampled similarly to the attainment 
curve. Therefore, we advise that similar sample densities are used in the deriva¬ 
tion of attainment and reference curves. As this is not always feasible, analytical 
methods are needed in the future to equally weight sampling densities between 
attainment and reference curves. 


4. In comparison with the current IDW spatial interpolation method, kriging 
represents a more robust method and was needed in our investigations on 
how spatial covariance affects CFD statistical inferences. Still, the IDW 
approach may sufficiently represent water quality data in many instances 
and lead to accurate estimation of attainment. A suggested strategy is to use 
a mix of IDW and kriging dependent upon situations where attainment was 
grossly exceeded or clearly met (IDW) versus more-or-less “borderline” 
cases (kriging). 

The current modeling approach for obtaining predicted attainment values in space 
is Inverse Distance Weighting (IDW), a non-statistical spatial interpolator that uses 
the observed data to calculate a weighted average as a predicted value for each loca¬ 
tion on the prediction grid. IDW has several advantages. It is a spatial interpolator 
and in general such methods have been shown to provide good prediction maps. In 
addition, it is easy to implement and automate because it does not require any deci¬ 
sion points during an interpolation session. IDW also has a major disadvantage - it 
is not a statistical method that can account for sampling error. 

Kriging is also a weighted average but first uses the data to estimate the weights 
to provide statistically optimal spatial predictions. As a recognized class of statis¬ 
tical methods with many years of dedicated research into model selection and 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-5 


estimation, kriging is designed to permit inferences from sampled data in the pres¬ 
ence of uncertainty. Thus the quantity and distribution of the sample data are 
reflected in those inferences. Indeed, the panel’s initial trials on the role of spatial 
sources of error in the CFD have depended upon the ability to propagate kriging 
interpolation uncertainty through the CFD process in generating confidence inter¬ 
vals of attainment. 

In comparison to IDW, kriging is more sophisticated but requires greater expertise 
in implementation. Kriging is available in commercial statistical software and 
also in the free open source R Statistical Computing Environment, and requires 
geostatistical expertise and programming skills for those software packages. 
Segment by segment variogram estimation and subsequent procedures would 
require substantial expert supervision and decision-making. Thus, this approach is 
not conducive to automation. On the other hand, there may be CBP applications 
where the decision on attainment is clearly not influenced to any substantial 
degree by the method of spatial interpolation. One suggested strategy is to use a 
mix of IDW and kriging - dependent upon situations where attainment was 
grossly exceeded or clearly met (IDW) versus more-or-less “borderline” cases 
(kriging). 


5. More intensive spatial and temporal monitoring of water quality will 
improve the CFD approach but will require further investigations on the 
influence of spatial and temporal covariance structures on the shape of the 
CFD curve. This issue is relevant in bringing 3-dimensional interpolations 
and continuous monitoring streams into the CFD approach. 

In the near future, the panel sees that the CFD approach is particularly powerful 
when linked to continuous spatial data streams made available through the cruise- 
track monitoring program, and the promise of continuous temporal data through 
further deployment of remote sensing platforms in the Chesapeake Bay (Chesa¬ 
peake Bay Observing System: http://www.cbos.org/). These data sets will support 
greater precision and accuracy in both threshold and attainment determinations 
made through the CFD approach but will require directed investigations into how 
data covary over different intervals of time and space. Further, there may be 
important space-time interactions that confound the CFD attainment procedure. 

Some of the assessments for the Bay such as that for dissolved oxygen require 
three dimensional interpolation, but the field of three dimensional interpolation is 
not as highly developed as that of two dimensional interpolation. Kriging can be 
advantageously applied in that it can use information from the data to develop 
direction dependent weighted interpolations (anisotropy). Kriging can include 
covariates like depth. Options for implementing 3-D interpolation include: 
custom IDW software, custom kriging software using GMS routines, or custom 
kriging software using the R-package. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-6 


RECOMMENDATIONS 

The panel identified critical research tasks that need resolution in the near future. 
The following is a list of critical aspects of that needed research. These research 
tasks appear roughly in order of priority. However, it must be recognized that it is 
difficult to formulate as set of tasks that can proceed with complete independence. 
For example, research on task 1 may show that the ability to conditionally simulate 
the water quality surface is critical to resolving the sample size bias issue. This 
discovery might eliminate IDW as a choice of interpolation under task 3. The Panel 
has made significant progress on several of these research tasks and CBP is encour¬ 
aged to implement continued study in a way that maintains the momentum 
established by our panel. 

TASK 

1. Effects of Sampling Design on CFD Results 

(a) Continue simulation work to evaluate CFD bias reduction via conditional 
simulation. 

(b) Investigate conditional simulation for interpolation methods other than 
kriging—this may lead to more simulation work. 

(c) Implement and apply interpolation with condition simulation on CBP data. 

2. Statistical inference framework for the CFD 

(a) Conduct confidence interval coverage experiments. 

(b) Investigate confidence interval methods for non-kriging interpolation 
methods. 

(c) Implement and evaluate confidence interval procedures. 

3. Choice of Interpolation Method 

(a) Implement a file system and software utilizing kriging interpolation for CBP 
data. 

(b) Compare interpolations and CFDs based on kriging and inverse distance 
weighting (IDW). 

(c) Investigate nonparametric interpolation methods such as LOESS and spline 
approaches. 

4. Three-Dimensional Interpolation 

(a) Implement 2-D kriging in layers to compare to current approach of 2-D IDW 
in layers. 

(b) Conduct studies of 3-D anisotrophy in CBP data. 

(c) Investigate software for full 3-D interpolation. 

5. High Density Temporal Data 

(a) Develop methods to use these data to improve temporal aspect of CFD 
implementation. 

(b) Investigate feasibility of 4-Dimensional interpolation. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-7 


1. INTRODUCTION 

In June 2000, Chesapeake Bay Program (CBP) partners adopted the Chesapeake 
2000 agreement (http://www.chesapeakebay.net/agreement.htm), a strategic plan 
that calls for defining the water quality conditions necessary to protect aquatic living 
resources. These water quality conditions are being defined through the development 
of Chesapeake Bay specific water quality criteria for dissolved oxygen, water clarity, 
and chlorophyll_a to be implemented as state water quality standards by 2005. One 
element of the newly defined standards is an assessment tool that addresses the 
spatial and temporal variability of these water quality measures in establishing 
compliance. This tool has become known as the Cumulative Frequency Diagram 
(CFD). 

The (CFD) was first proposed as an assessment tool by Paul Jacobson, of Langhei 
Ecology (www.LangheiEcology.com). At that time Dr. Jacobson was consulting 
with the Chesapeake Bay Program as a member of the Tidal Monitoring Network 
Redesign Team. Within this group, the CFD concept gained immediate recognition 
and support as a novel approach that permitted independent modeling of the time and 
space dimensions of the continuous domain that underlies Chesapeake Bay water 
quality parameters. In addition, because preparation of the CFD uses spatial inter¬ 
polation, the approach can allow integration of data collected on different spatial 
scales such as fixed station data and cruise track data. 

While the benefits of the CFD approach has been recognized (U.S. EPA 2003) and 
the the CBP has begun implementation of the approach for certain water quality 
parameters and segments of the Chesapeake Bay, investigations of the statistical 
properties revealed that the underlying shape parameters of the CFD were sensitive 
not only to rates of compliance but also to sampling design elements such as sample 
density. The novelty of the approach coupled with concerns about its statistical 
validity motivated the Chesapeake Bay Program to request that its Scientific and 
Technical Advisory Committee (http://www.chesapeake.org/stac/) empanel a group 
with expertise in criteria assessment, spatial data interpolation, and statistics to 
assess the scientific defensibility of the CFD. Here we report the findings of this 
panel. 

The primary goal of this panel is to provide an initial scientific review of the CFD 
compliance approach. This review addresses a wide range of issues including: bias 
and statistical rigor, uncertainty, practical implementation issues, and formulation of 
reference curves. Because of the novelty of the CFD approach, the panel has endeav¬ 
ored to research and explain the properties of the CFD and spatial modeling upon 
which the CFD approach depends to provide a basis for this evaluation. These activ¬ 
ities are beyond the scope of the typical review. However, because so little is known 
about the CFD, it was necessary to expand the knowledge base. 

The report is organized into 7 sections. In Section 2 of this report we present the 
CFD approach as a series of steps, each of which needs to be considered carefully in 
evaluating its statistical properties. Spatial interpolation is a critical but the most 
statistically nuanced step in the CFD approach. Spatial interpolation of water quality 
data in the CBP has to date received little statistical review. In Section 3 we evaluate 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 



A-8 


alternative geostatistical methods as they pertain to the CFD approach. The CFD 
approach is an attainment procedure, which depends upon statistical comparison 
between attainment and reference curves. In Section 4, we present alternative types 
of references curves and discuss statistical properties of each. In Section 5 the statis¬ 
tical properties of CFD curves (applicable to both attainment and reference curves) 
is elucidated through a series of conditional simulation trials. 

In addition to this primary charge, the panel is sensitive to the fact that the CFD will 
be employed in the enforcement of water quality standards. Use as a regulatory tool 
imposes a standard of credibility, which we review in Section 6. We use here “best 
available science” and “best science” criteria to evaluate the overall validity and 
feasibility of the CFD approach, following guidelines established by the American 
Fisheries Society and Estuarine Research Federation (Sullivan et al. 2006). These 
follow other similar criteria (e.g., The Daubert Criteria (Daubert v. Merrell Dow 
Pharmaceuticals, Inc., 1993) and include: 

1. A clear statement of objective 

2. A conceptual model, which is a framework for characterizing systems, sating 
assumptions, making predictions, and testing hypotheses. 

3. A good experimental design and a standardized method for collecting data. 

4. Statistical rigor and sound logic for analysis and interpretation. 

5. Clear documentation of methods, results, and conclusions 

6. Peer review. 

The panel has made progress in better understanding statistical properties of the 
CFD approach and overall, we recommend it as a feasible approach and one that 
qualifies under most criteria for best available science. Still, we believe that our 
efforts should only represent the beginning of a longer term effort to (1) Use simu¬ 
lations and other means to support statistical comparisons of CFD curves; and (2) 
Support the CBP’s efforts to model water quality data with sufficient rigor in both 
spatial and temporal dimensions. Research and implementation recommendations 
follow in Section 7. 


2.0 BACKGROUND 

2.1 THE CFD ASSESSMENT APPROACH 

The water quality criteria assessment methodology currently proposed by the E.P.A. 
Chesapeake Bay Program (CBP) involves the use of a Cumulative Frequency 
Diagram (CFD) curve. This curve is represented in a two dimensional plane of 
percent time and percent space. This document briefly discusses the reasoning that 
lead to the development of this assessment tool. The proposed algorithm for esti¬ 
mating the CFD is given and illustrated with small data sets. Some properties and 
unresolved issues regarding the use of the CFD are briefly discussed. In Section 5, 
simulation studies explore in greater specificity the multiple issues related to error 
and bias in the CFD approach. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 



A-9 


Reasoning Behind the CFD Approach 

The CFD assessment methodology evolved from a need to allow for variability in 
water quality parameters due to unusual events. For the water quality parameter to 
be assessed, a threshold criterion is established for which it is determined that water 
quality that exceeds this threshold is in a degraded state (For simplicity, we will 
speak of exceeding the threshold as representing degradation, even though for some 
water quality constituents such as dissolved oxygen, it is falling below a threshold 
that constitutes degradation). Because all water quality parameters are inherently 
variable in space and time, it is unlikely that a healthy bay will remain below the 
threshold in all places at all times. In the spatial dimension, there will be small 
regions that persistently exceed the threshold due to poor flushing or other natural 
conditions. It is recognized by CBP that these small regions of degraded condition 
should not lead to a degraded assessment for the segment surrounding this small 
region. Similar logic applies in the temporal dimension. For a short period of time, 
water quality in a large proportion of a segment may exceed the threshold, but if this 
condition is short lived and the segment quickly returns to a healthy state, this does 
not represent an impairment of the designated use of the segment. Recognition that 
ephemeral exceedances of the threshold in both time and space do not represent 
persistent impairment of the segment leads to an assessment methodology that will 
allow these conditions to be classed as acceptable while conditions of persistent and 
wide spread impaired condition will be flagged as unacceptable. The assessment 
methodology should first ask how much of the segment (for simplicity, a spatial 
assessment unit is called a segment, but more detail is given on spatial assessment 
units in Section 2) is not in compliance with the criteria (percent of space) for every 
point in time. In a second step the process should ask how often (percent of time) is 
a segment out of compliance by more than a fixed percent of space. The results from 
these queries can be presented in graphical form where percent of time is plotted 
against percent of space (Figure 2.1). It is arbitrary to treat space first and time 
second. A similar diagram could be obtained by first computing percent noncompli¬ 
ance in time and then considering the cumulative distribution of percent time over 
space. 

If a segment is generally in compliance with the criterion, then one expects a high 
frequency of dates where the percent out of compliance is low. In this case, the CFD 
should descend rapidly from the upper left corner and pass not too far from the lower 
left comer and then proceed to the lower right comer. The trace in Figure 2.1 shows 
the typical hyperbolic shape of the CFD. The closer the CFD passes to the origin 
(lower left corner), the better the compliance of the segment being assessed. As the 
CFD moves away from the origin, a higher frequency of large percents of space out 
of compliance is indicated. 

Formulating an Estimate of the CFD 

The algorithm developed by CBP for estimating the CFD is most easily described as 
a series of steps. These steps are given in bullet form to provide a frame work for the 
overall approach. The quickly defined framework is followed by a simple example. 
This in turn is followed by more detailed discussion of each step. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-10 



Figure 2.1. Illustration of CFD for 12 dates. 


The steps: 

1. Collect data from a spatial network of locations on a series of dates in a three 
year assessment period . 

2. For each date, interpolate the data for the entire system (e.g. mainstem bay) to 
obtain estimates of water quality in a grid of interpolation cells. 

3. For each interpolation cell assess whether or not the criterion is exceeded. 

4. For each assessment unit (e.g. segment), compute the percentage of interpolator 
cells that exceed the criterion as an estimate of the percent of area that exceeds 
the criterion. 

5. Rank the percent of area estimates for the set of all sample days in the assess¬ 
ment period from largest to smallest and sequentially assign to these ranked 
percents a value that estimates percent of time. 

6. Plot the paired percent of time and percent of area data on a graph with percent 
of area on the abscissa and percent of time on the ordinate. The resulting curve 
is the Cumulative Frequency Diagram. 

7. Compare the CFD from a segment being assessed to a reference CFD. If at any 
point the assessment CFD exceeds the reference CFD, that is, a given level of 
spatial noncompliance occurs more often than is allowed, then the segment is 
listed as failing to meet it’s designated use. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 










A-11 


Simple Numerical CFD Example 

For this example, assume a segment for which the interpolation grid is 4 cells by 4 
cells. In reality, the number of grid cells is much larger. Also let data be collected on 
5 dates. Typically data would be monthly for a total of 36 dates. Let the criterion 
threshold for this fictitious water quality parameter be 3. In what follows, you will 
Find an illustration of the steps of computing the CFD for these simplified 
constraints. The three columns of the next page show the first three steps. Column 
I shows fictional data for five dates for five fixed locations in a 2 dimensional grid. 
Column 2 shows a fictional interpolation of these data to cover the entire grid. 
Column 3 shows the compliance status of each cell in the grid where 1 indicates 
noncompliance and 0 indicates compliance. 


Step 1. Collect data at 
known locations. 


date 1 


3 



3 



5 






2 



1 

date2 

1 



1 



3 






1 



1 

date3 

4 



2 



2 






1 



1 

date4 

1 



4 



2 






4 



1 

date5 

1 



3 



2 






1 



1 


Step 2. Interpolate the 
data to grid cells. 


date 1 


3 

4 

5 

3 

4 

4 

5 

2 

3 

3 

4 

1 

2 

3 

3 

1 

date2 

1 

2 

3 

1 

2 

2 

3 

2 

1 

3 

2 

1 

1 

1 

1 

1 

date3 

4 

3 

2 

2 

3 

2 

2 

1 

2 

2 

1 

1 

1 

1 

1 

1 

date4 

1 

2 

3 

4 

2 

2 

2 

3 

3 

3 

2 

1 

4 

3 

1 

1 

date5 

1 

2 

3 

3 

9 

Z. 

2 

2 

2 

1 

1 

1 

1 

1 

1 

1 

1 


Step 3. Determine 
compliance status of each 
cell. 


date 1 


1 

1 

1 

1 

1 

1 

1 

0 

1 

1 

1 

0 

0 

1 

1 

0 

date2 

0 

0 

1 

0 

0 

0 

1 

0 

0 

1 

0 

0 

0 

0 

0 

0 

date3 

1 

1 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

date4 

0 

0 

1 

1 

0 

0 

0 

1 

1 

1 

0 

0 

1 

1 

0 

0 

date5 

0 

0 

1 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 

































































































































































A-12 


Step 4: Percent compliance by date. 


sample date 

percent 

space 

date 1 

75.00% 

date 2 

18.75% 

date 3 

18.75% 

date 4 

43.75% 

date 5 

12.50% 


Step 5. Rank the percent of space values 
and assign percent of time = (100*R/(M+1.0)), 
where R is rank and M is total number of dates. 


sample date 

ranked 

percent 

space 

cumulative 
percent time 

date 1 

75.00% 

16.67 

date 4 

43.75% 

33.33 

date 2 

18.75% 

50.00 

date 3 

18.75% 

66.67 

date 5 

12.50% 

83.33 


Steps 6 and 7: The plot of the CFD 
and the comparison to the reference 
curve are shown in Figure 2.2. For 
this hypothetical case the assessment 
area would be judged in noncompli¬ 
ance. For a percent area of 18.75, the 
allowable frequency on the reference 
curve is about 53%. That is, 18.75% 
of the segment area should not be out 
of compliance more that 53% of the 
time. For date 3, the estimated 
frequency of 18.75% noncompliance 
is 66.67%. Thus the frequency of 
18.75% of space out of compliance is 
in excess of the 53% allowed. The 
reference curve is exceeded for dates 
4 and 1 as well. Note: in this cumula¬ 
tive distribution framework, the actual 
date is not relevant. One should not 
infer that noncompliance occurred on 
that date if the data point associated 
with a date falls above the reference. 
Date is being used here as a label for 
each coordinate pair. 



Figure 2.2. Graphical representation of CFD from the above example (' + ') with hypothetical reference 
curve ( smooth). 






























A-13 


Defining the CFD Ideal 

As defined above, the CFD is a data driven formulation. But the data used to formu¬ 
late the CFD are a sample of points taken from a population. Defining the CFD 
becomes complex when one considers the many different levels for which it might 
be defined. At one level, the CFD might be defined based on the true state of a 
segment. Imagine that the state of a segment could be frozen for sufficient time to 
permit deployment of an analog sampler (that is one that measures water quality 
continuously rather than in discrete samples) to assess the percent of area out of 
compliance at that instant. Now stretch that imagination one step further to relax the 
condition that the segment be frozen and allow that these analog measurements of 
percent of area out of compliance be determined continuously in time. With this 
information, a determination of the CFD for the true state of the segment is possible. 
While the information needed to construct the ideal CFD is not obtainable, it is 
important to ask how well the CFD based on obtainable data represents this ideal 
(see also Section 5). Is a data driven CFD consistent for the ideal CFD in the statis¬ 
tical sense? Loosely speaking, consistency implies that the data driven CFD should 
get closer to the ideal CFD as more data are used. Is the data driven CFD unbiased 
for the ideal CFD? Unbiasedness implies that even with small amounts of data, the 
data driven CFD on average covers the ideal CFD. 

One might argue that if both the assessment CFD and the reference CFD are data 
driven, then it is not important for the CFD to approximate the ideal. Even so, it is 
important to understand the behavior of the CFD as a function of samples size and 
the relative temporal and spatial contributions to the variance in the water quality 
parameter. If the curve changes shape as a more data are used, this could result in 
unfair comparisons between assessment and reference regions. In Section 4, statis¬ 
tical properties for both types of reference curves are evaluated further. 

Defining Reference Curves 

Two approaches to defining the reference curve are being considered. One is a 
biologically based definition. The idea is to identify appropriate reference regions 
with healthy biological indicators and compute the reference CFD for these regions. 
For example, healthy benthic IBI scores might be used as indicators of adequate 
bottom dissolved oxygen. Thus after stratifying by salinity zone and perhaps other 
factors, a series of dissolved oxygen reference CDF curves could be computed from 
the existing 20+ year monitoring data base. When it is not possible to establish a 
reference condition some more arbitrary device must be employed. Alternatives are 
discussed in Section 4.0. 

Discussion of Each Step 

Step 1 - Data Collection. One of the advantages of the CFD approach is that it will 
accommodate a variety of input data and still arrive at the same assessment endpoint. 
Data collection methods currently in place include: fix station data, cruise track data, 
continuous monitor data, aircraft flight path data, and satellite imagery data. Because 
of the interpolation step, all of these data can be used (and potentially combined) 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-14 


with varying degrees of success to estimate the total spatial (to the limit of interpo¬ 
lator pixel size) distribution of a water quality constituent. As noted above, one could 
construct this process by reversing the roles of time and space. That is, first interpo¬ 
late over time and then build a cumulative distribution in space. In theory it is an 
abitrary choice to first standardize the data over space by interpolation and then 
construct the cumulative distribution in time. However, in practice, there is a greater 
diversity of sampling designs over space and therefore it is the sampling in the 
spatial dimension more than the temporal that creates many types of data that must 
be forced to a common currency. 

Step 2 - Interpolation. Interpolation is the step that puts data collected at various 
spatial intensities on a common footing. On the one hand, this is advantageous 
because data collected at many spatial intensities are available for the assessment 
process. On the other hand, it can be misleading to accept interpolated surfaces from 
different data sources as equivalent without qualifying each interpolation with a 
measure of the estimation error that is associated with each type of data. Clearly an 
interpolation based on hundreds of points per segment (such as cruise track data) will 
more accurately reflect the true noncompliance percent when compared to an inter¬ 
polation based on two or three points per segment (such a fixed station data). Of the 
various types of interpolation algorithms available, the method proposed for this 
assessment is kriging. Kriging offers the best available approach for the estimation 
error associated with interpolation. 

Step 3 - Pointwise Compliance. Determining the percent of compliance of each cell 
from each interpolation would seem to be a simple step. If the estimated value for a 
cell exceeds the criterion then that cell is out of compliance. 

While interpolation allows for a standardization of many types of data, pointwise 
compliance allows for standardization of many criteria. Because compliance is 
determined at points in time and space, it is possible to vary the compliance criteria 
in time and space. If different levels of a water quality constituent are acceptable in 
different seasons, then the criterion can vary by season. It is possible to implement 
different criteria over space for a segment that bridges oligohaline and mesohaline 
salinity regimes. It would even be possible to let the criterion be a continuous func¬ 
tion of some ancillary variable such as temperature or salinity. All that is required is 
that the final determination be yes or no for each interpolator cell. 

Even the simplicity of this concept becomes diminished when issues of interpolation 
error are considered. Consider the assessment of two interpolator cells from an inter¬ 
polation based on cruise track data. One cell near the cruise track has an estimated 
value is 4 and a standard error of 0.1. A second cell far from the cruise track has an 
estimated value of 4 and a standard error of 1.0. If the criterion were 3.0, it is fairly 
certain that the first cell represents exceedance. It is much less certain that the second 
cell represents exceedance. In the simple assessment of non-compliance, they count 
the same. 

Step 4 - Percent Non-compliance in Space. Computing a percentage should also 
be a simple step. The estimate is simply 100 times the number of cells out of compli¬ 
ance divided by the total number of cells. As a rule, the uncertainty of a binary 
process can be modeled using a binomial distribution. However, the issue of uncer- 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-15 


tainty described for step 3 propagates into computing the percent of compliance for 
a segment. Add to that the fact that estimated values for interpolator cells have a 
complex dependence structure which rules out a simple binomial model and the rules 
governing the uncertainty of this step are also complex. The number of interpolator 
cells, N, is relatively constant and under an independent binomial model the variance 
of the proportion of cells not in compliance, p, would be (p)(l-p)/N. Intuitively, one 
expects the variance of p to decrease as the number of data points that feeds the inter¬ 
polation increases. This expectation has been confirmed by simulation, but the 
mathematical tools for modeling this propagation of error are yet to be developed. 

Step 5 - Percent of Time. While the percent of space coordinate of the CFD has 
simple interpretation of the percent of the segment out of compliance on a given 
date, the percent of time coordinate is not simply the percent of time out of com¬ 
pliance at a given point. Instead the percent of time coordinate has an interpretation 
similar to that of a cumulative distribution function. The percent of time coordinate 
is the percent of time that the associated spatial percent of noncompliance is 
exceeded. For example, if the (percent space, percent time) coordinates for a point 
on the CFD are (90,10), one would say that the spatial percent of noncompliance is 
greater than or equal to 90% about 10% of the time. 

This step is very similar to computing an empirical distribution function which is an 
estimator of a cumulative distribution function. Because of this similarity, one imme¬ 
diately thinks of statistical inference tools associated with empirical distribution 
functions, such as the Kolmogorov-Smimov, Shapiro-Wilk, Anderson-Darling, or 
Cramer-von Mises, as candidates for inference about the CFD. These procedures 
model uncertainty as a function of sample size only; in this case the number of 
sample dates. The fact that it does not incorporate the uncertainty discussed the 
previous steps seems unsatisfactory. 

A quick review of probability plotting will reveal several methods on estimating the 
percent of time coordinate in step 5. Formulae found in the literature include: (R/N), 
(R - 0.5) / (N - 1). and (R - 0.375) / (N + 0.5), where R is rank and N is sample size. 
These generally fall in to a family of given by (R - A)/(N - 2A + 1) for various values 
of A. They are approximately equal, but the choice should be fixed for a rule. 

Step 6 - Plotting the CFD. Even the plotting of the points is subject to variation, 
although these variations are somewhat minor compared to the larger issue of 
assessing the uncertainty of the assessment curve. The simple approach used in the 
figures above is to connect the points by line segments. In the statistical literature, it 
is more common to use a step function. If the graph represents an empirical distri¬ 
bution function, each horizontal line segment is closed on the left and open on the 
right. Because the CFD is an inversion of an EDF it would be appropriate for these 
line segments to be closed on the right and open on the left. 

Step 7 - Comparing the Curves. It is at the point of comparing the assessment 
curve to the reference curve that the issue of uncertainty becomes most important. 
From the preceding discussion it is clear that uncertainty in the assessment curve is 
an accumulation of uncertainty generated in and propagated through the preceding 6 
steps. If the reference curve is biologically based, it is derived under the same system 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A*16 


of error propagation. Developing the statistical algorithms to quantify this uncer¬ 
tainty is challenging. 

Even if the uncertainty can be properly quantified, the issue of who gets the benefit 
of doubt due to this uncertainty is a difficult question to resolve. This is a broad 
sweeping issue regarding uncertainty in the regulatory process, not a problem 
specific to the CFD approach. None-the-less, it must be dealt with here as well as 
elsewhere. One option is to require that the assessment curve be significantly above 
the reference curve to establish noncompliance. This option protects the regulated 
party from being deemed out of compliance due to random effects, but if assessment 
CFD curves are not accurately determined, it could lead to poor protection of envi¬ 
ronmental health and designated uses. A second option is to require that the 
assessment curve be significantly below the reference curve to establish compliance. 
This results in strong protection of the environmental resource, but could lead to the 
regulated party implementing expensive management actions that are not necessary. 
Some compromise between these extremes is needed. The simplest compromise is 
to ignore variability and just compare the assessment curve to the reference curve. 
As long as unbiased estimation is implemented for both the assessment curve and the 
reference curve, this third option will result in roughly equal numbers of false posi¬ 
tive (declaring noncompliance when in fact compliance exists) and false negative 
(declaring compliance when in fact noncompliance exists) results. This offers a 
balanced approach, but there is no mechanism to motivate a reduction of these false 
positive and false negative errors. 


2.2 DATA AVAILABLE AND CURRENT METHODS 

OVERVIEW OF TYPES OF DATA AVAILABLE 

The Chesapeake Bay monitoring program routinely monitors 19 directly measured 
water quality paramenters at 49 stations in the mainstem Bay and 96 stations in the 
tidal tributaries. The Water Quality Monitoring Program began in June 1984 with 
stations sampled once each month during the colder late fall and winter months and 
twice each month in the warmer months. A refinement in 1995 reduced the number 
of mainstem monitoring cruises to 14 per year. “Special” cruises may be added to 
record unique weather events. The collecting organizations coordinate the sampling 
times of their respective stations, so that data for each sampling event, or “cruise”, 
represents a synoptic picture of the Bay at that point in time. At each station, a hydro- 
graphic profile is made (including water temperature, salinity, and dissolved oxygen) 
at approximately 1 to 2 meter intervals. Water samples for chemical analysis (e.g., 
nutrients and chlorophyll) are collected at the surface and bottom, and at two addi¬ 
tional depths depending on the existence and location of a pycnocline (region(s) of 
density discontinuity in the water column). Correlative data on sea state and climate 
are also collected. 

In addition, Chesapeake Bay Program partner organizations Maryland Department 
of Natural Resources and the Virginia Institute of Marine Science have recently 
begun monitoring using a technology known as data flow. DATAFLOW is a system 
of shipboard water quality probes that measure spatial position, water depth, water 


appendix a 


The Cumulative Frequency Diagram Method for Determining Watei Quality Attainment 



A-17 



Susquehanna R 


Baltimoie wm 
Pa taps co R. WT *' 


Waalxinaton 

S D.C. 


Potomac R 


«rrzt .Nt V > 

*ETZ3 irrJU; 
Tnit 'tw-p 
miB Cry 

TT12A. < 

Rappahannock R 


/'wfcomi co R 


LE11 i 

LE12^5 

LE13 ' 
i LE3 4 


Richmond Yoik R. 

TT 4.2 

TTV 2 O. 


Figure 2.3. Map of the tidal water quality monitoring stations. 


appendix a 


The Cumulative Frequency Diagram Method for 


Determining Water Quality Attainment 



















A-18 


temperature, salinity, dissolved oxygen, turbidity (clarity of the water), and chloro¬ 
phyll (indicator of plankton concentrations) from a flow-through stream of water 
collected near the water body’s surface. This system allows data to be collected 
rapidly (approximately every 4 seconds) and while the boat is traveling at speeds up 
to 20 knots. 

In 2005, the MDDNR Water Quality Mapping Program covered 16 Chesapeake Bay, 
Coastal Bay and Tributary systems. The St. Mary’s, Patuxent, West, Rhode, South, 
Middle, Bush, Gunpowder, Chester, Eastern Bay, Miles/Wye, Little Choptank, 
Chicamacomico and Transquaking Rivers will be mapped, as well as Fishing Bay 
and the Maryland Coastal Bays. In Virginia, dataflow data are available for the 
Piankatank, York, Pamunkey and Mataponi Rivers. 

Beginning in 1990, Chlorophyll-a concentrations were measured over the mainstem 
Chesapeake using aircraft remote sensing. From 1990-1995, the instrument used for 
this study was the Ocean Data Acquisition System (ODAS) which had three 
radiometers measuring water leaving radiance at 460, 490 and 520 nm. In 1996, an 
additional instrument was added, the SeaWiFS Aircraft Simulator (SAS II). SAS II 
has sensors at seen wavebands which improves detection of Chlorophyll in highly 
turbid areas. Since 1990, 25-30 flights per year have been made during the most 
productive times of year. 

The data described above and additional information can be obtained from: 
www.chesapekebay.netmddnr.chesapeakebay.net/eyesonthebay/index.cfm 

www2.vims.edu/vecos/ 

Description of the Current Nearest Neighbor/IDW Interpolator 

The current Chesapeake Bay Interpolator is a cell-based interpolator. Water quality 
predictions for each cell location are computed by averaging the nearest “n” neigh¬ 
boring water quality measurements, where “n” is normally 4, but this number is 
adjustable. Each neighbor included in the average is weighted by the inverse of the 
square of Euclidean distance to the prediction cell (IDW). Cell size in the Chesa¬ 
peake Bay was chosen to be 1km (east- west) x 1km (north-south) x 1m (vertical), 
with columns of cells extending from surface to the bottom of the water column, thus 
representing the 3-dimensional volume as a group of equal sized cells extending 
throughout the volume. The tributaries are represented by various sized cells 
depending on the geometry of the tributary, since the narrow upstream portions of 
the rivers require smaller cells to accurately model the river’s dimensions. This 
configuration results in a total of 51,839 cells by depth for the mainstem Chesapeake 
Bay (segments CB1TF-CB8PH), and a total of 238,669 cells by depth for all 77 
segments which comprise the mainstem Bay and tidal tributaries. 

The Chesapeake Bay Interpolator is unique in the way it computes values in 3 
dimensions. The interpolator code is optimized to compute concentration values, 
which closely reflect the physics of stratified water bodies, such as Chesapeake Bay. 
The Bay is very shallow compared to its width or length; hence water quality varies 
much more vertically than horizontally. The Chesapeake Bay Interpolator uses a 
vertical filter to select the vertical range of data that are used in each calculation. For 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-19 


instance, to compute a model cell value at 5m deep, monitoring data at 5m deep are 
preferred. If fewer than n (typically 4) monitoring data values are found at the 
preferred depth, the depth window is widened to search up to d (normally +/-2m) 
meters above and below the preferred depth, with the window being widened in 0.5m 
increments until n monitoring values have been found for the computation. The 
smallest acceptable n value is selectable by the user. If fewer than n values are 
located, a missing value (normally a -9) is calculated for that cell. A second search 
radius filter is implemented to limit the horizontal distance of monitoring data from 
the cell being computed. Data points outside the radius selected by the user 
(normally 25,000m) are excluded from calculation. This filter is included so that 
only data that are near the location being interpolated are used. 

In this version of the Interpolator, Segment and Region filters have been added. 
Segments are geographic limits for the interpolator model. For instance, the Main 
Bay is composed of 8 segments (CB1TF, CB20H, ...,CB8PH). The tributaries are 
composed of 77 additional segments, using the CBP 2003 segmentation. These 
segments divide the Bay into geographic areas that have somewhat homogeneous 
environmental conditions. This segmentation also provides a means for reporting 
results on a segment basis, which can show more localized changes compared to the 
whole Bay ecosystem. 

Segment and bathymetry information use by the interpolator is stored in auxiliary 
files. Segment information allows the interpolator to report results on a segment 
basis which can show more localized changes compared to the whole Bay 
ecosystem. These segment and bathymetry files have been created for the main bay 
and all of the larger tributaries. The CBP segmentation scheme was replicated in 
these files by partitioning and coding the interpolator cells that fall within each 
segment. 

The interpolator also identifies the geographic boundary that limits which moni¬ 
toring station data are included in interpolation for a given segment through a region 
file. Use of data regions ensures that the interpolator does not “reach across land” to 
obtain data from an adjacent river which would give erroneous results. By using data 
regions, each segment of cells can be computed from their individual subset of moni¬ 
toring data. Each adjacent data region should overlap by some amount so that there 
is a continuous gradient, and not a seam, across segment boundaries. 

Current Implementation of CFD 

The Chesapeake Bay Program has initiated implementation of the CFD as an assess¬ 
ment tool. The Criteria Assessment Protocols (CAP) workgroup was formed in the 
fall of 2005 to develop detailed procedures for implementing criteria assessment. 
This workgroup has developed and implemented procedures that use the CFD 
process and conducted a CFD evaluation of dissolved oxygen for many designated 
assessment units. 

The CFD methodology was first applied in the Chesapeake Bay for the most recent 
listing cycle which was completed in the Spring of 2006 and was based on data 
collected over the period 2002 through 2004. CFDs were developed and utilized 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-20 


primarily for the dissolved oxygen (DO) open- and deep-water monthly mean 
criteria because there were insufficient data collected to assess the higher-frequency 
DO criteria components. The clarity criteria were not assessed based on the CFD 
because there were few systems in which there was sufficient data for an assessment. 
Chlorophyll criteria were not available from the Chlorophyll criteria team in time to 
implement a chlorophyll assessment. 

In general, the CFD analysis indicated that most of the Bay waters failed one or more 
of the open-water or deep-water DO criteria components. However, there were also 
many tributaries in which all of the DO criteria assessed indicated attainment. Thus 
in this initial application, the CFD method did appear to distinguish between 
impaired and unimpaired systems in a manner that is consistent with the expectations 
of the many stakeholders in the CAP workgroup. 

In the 2006 application of the assessment methodology, there were many details that 
required resolution in order to fully implement the methodology. Procedures gener¬ 
ally followed the theoretical description as described in Section 2.1, but some details 
were modified to address unforeseen complications. The following describes some 
of those details. 

In general, data were obtained from the CBP CIMS data base and parameters 
included date, location, depth, salinity, temperature and the water quality parameter 
being assessed. Some State data were also incorporated and those data were obtained 
directly from the relevant State. Once all the data were compiled, they were assigned 
to a time period based on the sample date. Fixed-station data are normally collected 
during a monitoring cruise that covers the entire tidal Chesapeake Bay over several 
days. However, in order to provide a “snapshot” in water quality, the data collected 
within a cruise are assumed to be contemporaneous in order to perform a single 
spatial interpolation. For any data not associated with a cruise, a cruise number is 
assigned representing the closest cruise in time to the collection of each datum. Co- 
located data points in the same cruise were averaged. 

The assessment procedure requires assessment over large areas rather than at points 
in space. Spatial interpolation using the CBP IDW interpolator was performed for 
each water-quality criteria parameter for each cruise. Clarity and surface chlorophyll 
were interpolated in the two horizontal dimensions using inverse distance squared 
weighting. Dissolved oxygen was first linearly interpolated in the vertical dimension 
within each column of data beginning at 0.5 meters and continuing at one meter 
intervals, not to exceed the deepest observation in that column. Each depth was then 
interpolated horizontally using inverse distance squared weighting. Data regions 
were specified for each segment in order to prevent the interpolation algorithm from 
using data points in neighboring tributaries. 

Designated uses in the Chesapeake Bay are defined vertically in order separate stable 
water layers that have differing criteria levels for dissolved oxygen. The surface layer 
(open water) is that layer defined to be above the pycnocline and thus exposed to the 
atmosphere. The middle layer (deep water) is defined to be the layer between the 
upper and lower pycnocline. And the lower layer (deep channel) is defined to be the 
layer below the pycnocline. Given that the pycnocline is dynamic and moves up and 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-21 


down with each monitoring cruise, the designated use of each grid cell must also be 
defined based on the available data for each cruise. 

The pycnocline is defined by the water density gradient over depth. Temperature and 
salinity are used to calculate density, which in turn is used to calculate pycnocline 
boundaries. Density is calculated using the method described in: Algorithms for 
Computation of Fundamental Properties of Seawater (Endorsed by 
UNESCO/SCOR/ ICES/IAPSO Joint Panel on Oceanographic Tables and Standards 
and SCOR Working Group 51. Fofonoff, N P; Millard, R C Jr. UNESCO technical 
papers in marine science. Paris , no. 44, pp. 53. 1983). For each column of temper¬ 
ature and salinity data, the existence of the upper and lower pycnocline boundary is 
determined by looking for the shallowest robust vertical change in density of 0.1 
kg/m3/m for the upper boundary and deepest change of 0.2 kg/m3/m for the lower 
boundary. To be considered robust, the density gradient must not reverse direction at 
the next measurement and must be accompanied by a change in salinity, not just 
temperature. 

The depths to the upper pycnocline boundary, where detected, and the fraction of the 
water column below the lower boundary are interpolated in two dimensions. If no 
lower boundary was detected the fraction was considered to be zero. The depth to the 
upper pycnocline boundary tends to be stable across horizontal space and so spatial 
definition of that boundary using interpolation generally worked well. However, 
interpolation of the lower boundary is more complicated because the results can 
conflict with the upper boundary definition or with the actual bathymetry of the Bay. 
As a result, interpolation of the lower boundary was performed based on “fraction of 
water column depth". In that way, the constraints of the upper pycnocline boundary 
definition and the actual depth were imposed and errors related to boundary conflicts 
were eliminated. 

Assessments were performed based on criteria specific averaging periods. The 
instantaneous assessment for deep channel dissolved oxygen was evaluated using the 
individual cruise interpolations. All monthly assessments were based on monthly 
averages of interpolated data sets. To calculate the monthly averages, each interpo¬ 
lated cruise within a month was averaged on a point-by-point basis. Generally, there 
were 2 cruises per month in the wanner months and 1 cruise per month in the cooler 
months. Spatial violation rates are calculated for each temporally aggregated inter¬ 
polation in an assessment period. For example, for a three-year summer open-water 
dissolved oxygen assessment, the twelve monthly average interpolations repre¬ 
senting the four summer months over three years were used. 


3. PROTOCOL FOR INTERPOLATING WATER QUALITY 

The CFD approach uses the proportion of space in attainment in any given month 
estimated using an approach based on a statistical model. The current method uses 
data collected in a specific month at a set of sampling locations within the segment 
of interest to estimate the parameters of the model. The estimated model is then used 
to interpolate likely values at unsampled locations, specifically at a set of prediction 
locations arranged in a grid over the segment. The predictions thus obtained are used 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 



A-22 


to calculate the proportion of space in compliance that month. The current estima¬ 
tion procedure for obtaining predicted values is Inverse Distance Weighting (IDW), 
a non-statistical spatial interpolator that uses the observed data to calculate a 
weighted average as a predicted value for each location on the prediction grid. The 
method calculates the weight associated with a given observation as the inverse of 
the square of the distance between the prediction location and the observation. 

The panel considered several interpolation methods in addition to IDW. Of these, 
kriging methods emerged as a principal alternative approach for populating the grid 
of prediction locations. Non-parametric methods were also considered. These 
include Loess regression or cubic spline methods. These approaches could be advan¬ 
tageous in that they are statistical methods that provide levels of error, but panel 
analyses and deliberations have been insufficient to provide definitive statements on 
this class of methods. Table 3.2 which appears in Section 3.3 summarizes our deter¬ 
minations. 

3.1 KRIGING OVERVIEW 

Kriging is a spatial interpolation technique that arose out of the field of geostatistics, 
a subfield of statistics that deals with the analysis of spatial data. Kriging and the 
field of geostatistics has been employed in a wide variety of environmental applica¬ 
tions and is generally accepted as a method for performing statistically optimal 
spatial interpolations (Cressie 1991, Schabenberger and Gotway 2004, Diggle and 
Ribeiro 2006). Applications of kriging in water related research can be found in 
(Kitanidis 1997, Wang and Liu 2005,0uyang et al. 2006). References on kriging 
methodology, geostatistics, and their related statistical development can be found in 
(Cressie 1991, Diggle et al. 1998, Schabenberger and Gotway 2004, Diggle and 
Ribeiro 2006). 

Kriging can equivalently be formulated in terms of a general linear regression model 

Y (s) =/3 0 + £, X|(s) • • • +/3 p X p (s) + e(s) (1) 

with s representing a generic spatial location vector (usually 2-D) assumed to vary 
continuously over some domain of interest, Y(s) the outcome of interest measured at 

s, X](s).X p (s,) potential covariates indexed by location s, and their associated 

regression effects /3j, . . . , /3 p . Note that covariates must be known at every predic¬ 
tion location. The elements of the spatial vector s can be used as covariates for 
modeling spatial trends. On the other hand water quality measures such as salinity 
which may have a strong association with the outcome of interest, is of limited value 
as a covariate because it is not known at all prediction locations. The uncertainty in 
this regression relationship is modeled with the random error term e{s ) assumed to 
have zero mean and constant variance. Spatial data like the type sampled in the 
Chesapeake Bay water-quality criteria assessments often exhibit a property known 
as (positive) spatial dependence, observations closer together are more similar than 
those further away. This property is accounted for in model (1) by allowing e(s) to 
have a spatial correlation structure. 

Some further specifics on e(s) are warranted. Common distributional assumptions on 
e(s ) include normality or log-normality, although kriging can be performed based on 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-23 


other statistical distributions and data transformations (Christenson et al. 2001). The 
spatial correlation in e(s) is represented by positive definite functions. These func¬ 
tions can be assumed isotropic where correlation decay depends just on distance, or 
anisotropic where correlation decay depends on distance and direction. Variograms 
are another special type of mathematical function closely related to spatial correla¬ 
tion functions that can and are more often used to represent spatial correlation. For 
purposes here and in many kriging applications, variograms and spatial correlation 
functions provide equivalent representations of spatial structure. For consistency in 
what follows only the term variogram will be used in discussions of spatial structure. 

While there is considerable flexibility in implementing the error structure of a 
kriging model, it is possible to generalize somewhat with respect to the error struc¬ 
ture of Chesapeake Bay water quality data. Of the three water quality parameters 
being assessed, chlorophyll and clarity measures tend to follow the log-normal 
distribution and dissolved oxygen is reasonably approximated by the normal distri¬ 
bution. The horizontal decay rate of spatial correlation does not tend to be 
directionally dependent. Thus if the bay is viewed as a composite of horizontal 
layers, isotropic variograms are appropriate for kriging each layer. In a vertical direc¬ 
tion, water quality can change rapidly and thus spatial correlation can decay over a 
short distance. A 3-D interpolation procedure would benefit from use of an 
anisotropic variogram in order to differentiate the vertical correlation decay from the 
horizontal correlation decay. 

Note, in the literature model (1) is referred to as a universal kriging model. When 
covariates (the X’s) are not considered to influence interpolation of Y the right hand 
side of model (1) contains just the constant term /J 0 and e(s). The resulting model is 
referred to as the ordinary kriging model. When the spatial structure (variogram) for 
model (1) is known, statistically optimal predictions for the variable Y at unsampled 
locations (outside of estimation of possible regression effects) can be derived using 
standard statistical principles. The optimality criteria results in spatial predictions 
that are linear in the data, statistically unbiased, and minimize mean squared predic¬ 
tion error, hence referred to as best linear unbiased predictions (BLUPs). The 
minimized mean squared prediction error is also taken as a measure of prediction 
uncertainty. In practice, however, spatial structure of the data is unknown, the esti¬ 
mation of which via the variogram function is cornerstone to kriging applications. 

To demonstrate let {>’(si), . . . , y(s n )} represent a set of spatial data, for example a 
water-quality parameter such as dissolved oxygen sampled at a set of n spatial loca¬ 
tions S|, . . . , s n . Assume this data to be a realization of the ordinary kriging version 
of model (1). The first step in kriging is variogram estimation. There are several 
methods available, method of moments and statistical likelihood based being two of 
the more common, all of which though are based on the sample data {_y(sj), . . . , 
v(s n )}. Without going into detail, this process ends with a chosen variogram function 
and its parameter estimation, describing the shape and strength (rate of decay) of 
spatial correlation. There is also a determination, again based on the sampled data, 
of whether the spatial structure is isotropic or anisotropic. The estimated variogram 
is then assumed known and kriged interpolations and their interpolated uncertainty 
are computationally straight forward to generate at numerous locations where data 


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A-24 


were not observed. Accounting for uncertainty in variogram parameter estimation 
has commonly been explored using Bayesian methods (Diggle and Ribeiro 2006). 

3.2 IDW OVERVIEW 

The inverse distance weighting method that is currently used in the CFD approach 
has already been described. Hence, this section provides a short review of IDW’s 
technical details and a comparison of IDW to alternative interpolation methods. 

The IDW method is essentially a deterministic, non-statistical approach to interpo¬ 
lating a two or three dimensional space. As a result it lacks statistical rigor so that 
estimates of the prediction errors are not calculable without additional assumptions. 
Similar to kriging, IDW predicts a value () at an unobserved site, say at location .v 0 , 
using a weighted average of the N nearest observed neighbors (N specified by the 
modeler): 


N 

Y(s 0 ) = ^w,Y(s,) 

i=l 

where the weights, w„ are inversely related to the distance between locations s 0 and s, 

d(s 0 ,s ,)' 2 

W , =“N- f 

J=1 

d(s 0 ,Sj) is the Euclidean distance between locations s 0 and s,, and the denominator of 
the weight is to ensure that the weights sum to 1. The IDW is an exact interpolator 
in that the predicted values for observed locations are the observed values and the 
maximum and minimum values of the interpolated surface can occur only at 
observed sites. 

Recent research has compared IDW to other interpolation techniques, most notably 
variations in kriging (Table 3.1). The authors found that in some cases kriging was 
at least as good an interpolator as IDW and in some instances better. The non-para- 
metric techniques (splines and similar methods) were not as precise as kriging and 
IDW. The method used for comparison in virtually all of the research was some 
variant of cross-validation, a method where some data are kept aside and not used in 
the model estimation phase and then using the resulting model to predict values for 
the data kept aside. The predicted and observed values are then compared and a 
statistic is calculated that summarizes the differences between the two sets of values 
(observed and predicted). 

None of these studies used datasets with highly irregular edges such as are found in 
the Chesapeake Bay nor did they use any distance metric other than Euclidean 
distance. Whether one method is preferable to another in these more difficult situa¬ 
tions remains unexplored. 


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Table 3.1. A short list of recent articles comparing the precision of IDW to a subset of 
other possible interpolation methods. 


Authors 

Methods Compared 

Variables 

Manipulated 

Conclusions 

Kravchenko(2003) 

Inverse Distance 
Weighting (IDW), 
Ordinary Knging 
(OK) 

spatial structure and 
sample grid spacing 

IDW better than OK 
unless sample sizes 
were fairly large 

Dille. et al. (2002) 

IDW. OK, Minimum 
Surface Curvature 
(MC). Multiquadric 
Radial Basis Function 
(MUL) 

neighborhood size, 
spatial structure, 
power coefficient in 
IDW, sample grid 
spacing, quadrat size 

No interpolator 
appears to be more 
precise than another. 
Sample grid spacing 
and quadrat size were 
deemed more 
important. 

Valley, et al. (2005) 

IDW, OK. Non- 
parametric Detrend + 
Splines 

spatial structure, 
sample size, quadrat 
size 

OK tended to be more 
precise but IDW was 
very similar 

Lloyd (2005) 

moving windovs- 
Regression (MWR), 
IDW, OK. simple 
kriging with locally 
varying mean (SKlm). 
kriging with external 
drift (KED) 

spatial structure, 
sample size 

KED and OK best 

Remstorf, et al. 

(2005) 

IDW, OK, KED + 
deterministic 
chemical transport 
models 

single dataset was 
analyzed 

OK best 

Zimmerman, et al. 
(1999) 

2 types of IDW, UK. 
OK 

spatial structure, 
sampling pattern, 
population variance 

UK and OK better 
than IDW 


One final and important issue with IDW is that, as currently used, IDW is a deter¬ 
ministic method which makes no assumptions as to the probability distribution of the 
data being interpolated. Hence, it does not allow for estimating prediction errors, i.e. 
it does not allow for the possibility of random variation at interpolation sites. A 
simple question is whether IDW can be recast in the knging framework given the 
similarity in prediction method (weighted average) and hence can a method be found 
to estimate prediction errors? The short answer is no - the distance function used by 
IDW. which is an implicit assumption about the autocorrelation function in the 
spatial field, does not meet the assumptions required for development of a valid vari¬ 
ance-covariance matrix describing the spatial covariance. As a result. IDW cannot be 
modified to take advantage of the statistical knowledge that has been developed for 
geostatistical analyses such as kriging. This does not imply that other approaches to 
estimating prediction error are also not possible. 

A non-parametric approach for estimating variance was proposed (Tomczak, 1998) 
in which jack-knifing was used to provide error estimates. 95% confidence intervals 
for the mean were calculated and then compared to the actual observed values. Not 
surprisingly, only 65% of the data were captured within their associated confidence 
interval. The method appears to have been misapplied—the jackknifing method as 


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used estimates the standard error of the mean assuming independent observations. 
As a result, the confidence interval is not capturing the effect of the spatial depend¬ 
encies nor is it based on the fact that we are predicting a value for the unobserved 
site rather than estimating a mean. The development described by Tomczak (1998) 
should be explored further and other alternatives such as block bootstrapping for 
variance estimation as well. 

3.3 NON-PARAMETRIC INTERPOLATION METHODS 

There are many variations on spatial interpolation in addition to kriging and IDW. 
See Cressie (1989) for a review. The committee did not have sufficient time to 
compare all models, but CBP in encouraged to continue this research. One promising 
category of models are for interpolation based on non-parametric methods that do 
not rely on measuring and accounting for spatial autocorrelation. All of the non-para¬ 
metric approaches would be based on the assumption that the autocorrelation 
observed in the data is due to unobserved explanatory variables and hence alterna¬ 
tive modeling approaches are not unreasonable. The particular set we mention are 
the regression type analyses with the locational indices (northings, eastings) used as 
explanatory variables. Examples include generalized additive models (Hastie and 
Tibshirani, 1990), high-order polynomials (Kutner, Nachtsheim, Neter, and Li, 
2004), splines (Wahba, 1990), and locally weighted regression (“loess” or “lowess”, 
Cleveland and Devlin, 1988). In some kriging and IDW methods, large-scale trend 
is modeled relatively smoothly using locational indices and local smaller-scale vari¬ 
ation is modeled using the estimated autocorrelation in conjunction with the values 
of the variable at nearby observed sites. The nonparametric methods replace estima¬ 
tion of the local variation based on correlation functions with models of the 
large-scale trend that are less smooth and more responsive to the spatial variation in 
the observed data. A visual demonstration is given in Figure 3.1 which shows a one¬ 
dimensional dataset with Y as the variable to be predicted and X as the location along 
the one dimensional axis. For example, X could be distance from the mouth of a river 
and Y could be chlorophyll a concentration. 

One advantage of these approaches is that each of the methods has extensive statis¬ 
tical research into estimation of model parameters as well as standard errors for those 
parameters and for predictions at interpolation sites. Another is that the main 
modeling decisions are related to bandwidth selection or degree order of polynomial 
to fit. These decisions can be automated by developing rules for roughness of fit 
based on reduction in MSE as compared to modeling a straight line (in X). Disad¬ 
vantages are the same as for kriging, all model estimation is data dependent which 
means that the spatial configuration and number of sampling sites has a direct influ¬ 
ence on the predictions and their error estimates. In addition, a study done by Laslett 
(1994) comparing kriging and splines indicated that the two methods are similar in 
predictive power but for certain sampling regimes kriging performs better. We 
recommend more study since the non-parametric approaches would be easier to 
implement than kriging. 


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0 10 20 30 40 50 60 70 80 90 

X 


Figure 3.1. Bivariate fit of Y By X. Straight line is a linear large-scale trend fit (R 2 = 0.19); 
the moderately wavy line around the straight line is a 6th-order polynomial fit (X enters 
the model as X, X 2 , X 3 , and X 6 ; R 2 = 0.25); and the jagged line is a spline fit with a 
very small bandwidth (neighborhood used in local estimation at each X; R 2 = 0.90). 


3.3 COMPARISON OF METHODS 

The following describes some of the benefits and potential limitations of kriging in 
regards to CBP application with some comparisons to the IDW approach towards 
spatial interpolation outlined in the previous section. Nonparametric methods are not 
sufficiently developed to include in this comparison. A primary benefit of the kriging 
methodology compared to IDW is that it is a statistical technique. As such the field 
of statistics (including kriging) is designed to make inference from sampled data in 
the presence of uncertainty and the quantity and quality of the sample data are 
reflected in those inferences. However, kriging is a less than routine type of statis¬ 
tical analysis and requires a certain level of statistical expertise to carry out the 
process. The short description on variogram estimation provided above merely intro¬ 
duces this involved and often complicated step. This requirement for informed 
decision making limits the degree to which kriging can be automated and still main¬ 
tain its flexibility and optimal properties. 

Further issues regarding kriging and CBP applications are listed below. 

• Kriging is flexible in that it is based on an estimate of the strength of spatial 
dependence in the data (variogram). Kriging can consider direction dependent 
weighted interpolations (anisotropy) and can include covariates (universal 
kriging) to potentially influence interpolations, either simple trends in easting 
and northing coordinates or water related measures such as sea surface temper¬ 
ature measured by satellite. 

• A key feature of a statistical technique like kriging is that a measure of uncer¬ 
tainty (called the kriged prediction variance) is generated along with kriged 
interpolations. Research has been initiated (i.e., conditional simulation) to 


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A-28 


propagate this interpolation uncertainty through the CFD process for gener¬ 
ating confidence intervals for estimates of attainment. 

• Kriging can be applied in situations where the data are sparse, as in CBP fixed 
station data, or densely sampled, as in CBP shallow water monitoring. Kriged 
and IDW spatial interpolations may very well produce near identical results for 
these two extreme scenarios. However it is the kriging approach that provides 
a statistical model, the uncertainty of which is influenced by the quantity and 
quality of data. Knowledge of interpolation uncertainty is crucial for discrimi¬ 
nating the improved water quality assessment obtained from densely sampled 
networks relative to sparsely sampled networks. 

As alluded to earlier kriging is an advanced statistical technique and like all such 
techniques should be carried out by well trained statistician(s) with experience in 
spatial or geostatistical methodology and experience analyzing water quality data. 
Assessing model fits (of the variogram and regression model) and kriging accuracy 
via cross validation and/or likelihood based criteria should be employed routinely. 

To further exemplify this point consider kriging the densely sampled shallow water 
monitoring data which is generated by the DATAFLOW sampling. In addition to the 
other technical complexities mentioned within, this spatial sampling design may 
raise other issues not immediately recognized by untrained users (Deutsch 1984). 

For kriging in CBP applications one potential methodological drawback is the issue 
of non-Euclidean distance (Curriero 2006). Current kriging methodology only 
allows the use of the straight line Euclidean distance as the measure of proximity. 
However, the irregular waterways in the Chesapeake Bay system may very well 
suggest other non- standard measures of distance. For example, the spatial design of 
the fixed station data including those in the Bay mainstem and tidal tributaries. The 
straight line Euclidean distance may very well intersect land particularly in regions 
containing convoluted shorelines. There has been research initiated on this topic 
(Curriero 2006, Jensen et al. 2006, Ver Hoef et al. 2007), however, results are not yet 
ready for universal use. 

Three dimensional interpolations (including depth as the third dimension) are poten¬ 
tially required for CBP applications. The IDW and kriging methodologies, 
mathematically speaking, certainly extend to three dimensions. However the rapid 
change of water quality over depth would lead to significant anisotropies in the 
application three dimensional kriging that would complicate this approach far more 
than the application of IDW. On the other hand, a simplistic implementation of IDW 
that does not recognize the rapid decay of covariance over depth would inappropri¬ 
ately reach across the pycnocline when choosing nearest neighbors. Clearly the 
special properties of water quality in a highly stratified bay require innovation for 3- 
dimensional interpolations. Another approach would be to apply universal kriging 
where a third dimension (depth) is used as a covariate. The use of depth as an inde¬ 
pendent variable is motivated by the observation that often water quality exhibits a 
predictable trend over depth as for example the trend of DO decreasing with 
increasing depth. To include depth as a covariate, model (1) would be written as 

Y (s) = fio + /J]Depth(s) + y (s): 


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A third approach to interpolation in three dimensions is to implement 2-D interpola¬ 
tion in layers. Note that the IDW interpolator currently implemented by CBP 
(Section 2.2) employs a layered strategy by severely restricting (+/- 2m) the vertical 
distance that may be searched for nearest neighbors. A similar strategy could be 
implemented using 2-D kriging to interpolate the layers. Which of these approaches 
is best suited to 3-D interpolation for the bay will depend on the data available and 
assumptions related to vertical structure. Full 3-D kriging interpolation treats the 3rd 
dimension as a spatial dimension in the error term y ( s ). The covariate approach 
requires that the change over depth be a predictable trend. Interpolation in layers 
assumes that covariance decays so rapidly over depth that it is adequate to treat the 
layers as independent entities. Data sufficiency requirements increase for all 
approaches when considering three dimensional interpolations. When data are 
sparse, again a statistical based approach like kriging allows this to be reflected in 
prediction uncertainty. 

In many applications, attainment or lack of attainment will be so extreme that the 
assessment end point is clear even without optimizing the error estimation of the 
CFD. In these extreme cases, IDW or kriging simplified for automation could be 
sufficient to support the attainment ruling without precise quantification of estima¬ 
tion uncertainty. For these cases, the customized IDW algorithm that is currently 
implemented by CBP provides a tool with which to begin testing the CFD assess¬ 
ment procedure, but kriging simplified for automation may offer some advantages. 
Kriging can be simplified for automation by fixing the variogram model to one math¬ 
ematical form, say exponential, for all applications. With the variogram model fixed, 
kriging becomes like IDW in assuming the same mathematical form for the spatial 
dependence for all cases, but it is more flexible than IDW in that the rate of spatial 
correlation decay could be allowed to vary among applications. In addition, the 
simplified kriging opens the door for conditional simulation, with potential benefits 
that are discussed in Section 5. While a simplified kriging algorithm offers some 
advantages, there are also some potential drawbacks. Because variogram estimation 
typically entails use of an iterative procedure such as maximum likelihood or non¬ 
linear least squares, there is the potential that lack of convergence of these 
algorithms would be problematic for an automated implementation of kriging. 

In terms of computing, IDW is available in commercial GIS software, requiring GIS 
skills for application. Kriging is available in commercial statistical software and also 
in the free open source R Statistical Computing Environment (R Development Core 
Team 2005, Ribeiro and Diggle 2001) and requires programming skills for those 
software packages. 

In summary, kriging is more sophisticated than IDW, but requires greater expertise 
during implementation to fully exploit its full benefit. Table 3.2 provides a com¬ 
parison of the capabilities of assessments based simply on: 1) percent of samples, 
2) spatial interpolation based on IDW and 3) spatial interpolation based on kriging. 


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Table 3.2. Comparison of the capabilities of methods available for interpreting data 
collected for Chesapeake Bay water-quality criteria assessment. 


Attributes 

Sample-based 

IDW 

Kriging 

Provides Spatial 

Prediction 

Yes 

Yes 

Yes 

Provides Prediction 
Uncertainty 

No 

not routine 

Yes 

Uncertainty for CFD 

No 

No 

Yes 

Deal with Anisotropy 

No 

Possible, but 
not routine 

Yes 

Can Include Cruise 
Track/Fly over 

No 

Yes 

Yes 

Feasibility of 3 

dimensional 

interpolations 

No 

Yes 

Possible, but not 
routine 

Feasibility of mainstem- 
tributary interpolations 

No 

Yes 

Possible 

Inclusion of covariates to 
improve prediction 

No 

No 

Yes 

Predictions of non-linear 
functions of predicted 
attainment surfaces 

P(y>c) 

No 

No 

Yes 

Level of Sophistication 

Lowest 

Low 

Very High 

Automation 

Yes 

Yes 

Possible, but not 
routine 


4.0 CFD REFERENCE CURVES 

There are several approaches to defining reference curves that are proposed for use 
in the CFD assessment methodology. One is a biologically based definition and other 
approaches are based on an arbitrary allowable frequency (see Section 2). Here we 
review these options in greater detail. 

4.1. BIOLOGICAL REFERENCE CURVES 

The idea behind biological reference curves is to identify regions of the Bay that 
have healthy biological indicators and are thus considered to be in attainment of their 
designated use. CFDs would be developed for these areas in the same way that CFDs 
would be developed elsewhere, but those curves developed for healthy areas would 
be considered “reference” curves. For example, healthy benthic IBI scores might be 
used as indicators of adequate bottom dissolved oxygen. 

The success of the CFD-based assessment will be dependent upon decision rules 
related to the biological reference curves. These curves represent desired segment- 
designated use water quality outcomes and reflect sources of acceptable natural 


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variability. The reference and attainment curves follow the same general approach in 
derivation—water quality data collection, spatial interpolation, comparison to 
biologically-based water quality criteria, and combination of space-time attainment 
data through a CFD. Therefore, the biological reference curve allows for implemen¬ 
tation of threshold uncertainty as long as the reference curve is sampled similarly to 
the attainment curve. Bias and uncertainty are driven in CFD curves by sample 
densities in time and space. Therefore, we advise that similar sample densities are 
used in the derivation of attainment and reference curves. As this is not always 
feasible, analytical methods are needed in the future to equally weight sampling 
densities between attainment and reference curves. 

4.2. CBP DEFAULT REFERENCE CURVE 

In some cases, the development of biologically-based reference curve is not possible 
due to lack of data describing the health of the relevant species. In such cases, a more 
arbitrary approach is required since better information is not available. EPA recom¬ 
mends the use of a default curve in cases where a biologically-based one is not 
available. That default curve is defined by these properties: 

1. symmetric about the 1:1 line, 

2. hyperbolic, 

3. total area = 0.1, and 

4. pass through (1,0) and (0,1) 

(see EPA, 2003; page 174). The equation that describes this figure is defined by the 
equation: 

(x+b) * (y+b) = a 

Where: b = 0.0429945 
a = b : + b 

This reference curve is illustrated in Figure 4.1 by curve 1. 

An alternative default reference curve might be formulated by extending the arbi¬ 
trary allowance of 10% exceedance into the two dimensional framework of the CFD. 

The criterion threshold is a value that should be rarely exceeded by a population at 
healthy levels. When the population is unidimensional, say concentration in a point 
source effluent, then one can obtain this upper threshold based on the simple distri¬ 
bution of values in a healthy population (Figure 4.2). The ninetieth percentile of this 
distribution might be chosen as the criterion threshold. Thus in this example, 10% 
noncompliance is allowed because this level of noncompliance is expected in a 
healthy population. A standard technique for estimating distribution percentiles is to 
assume a mathematical form for the distribution, e.g., the normal distribution, and to 
estimate the percentile as some number of standard deviations above the mean. The 
90th percentile of the normal distribution is 1.2815 standard deviations above the 
mean. 


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Figure 4.1. Illustrations of three reference curves: 1) the standard CBP reference curve 
derived to cover 10% of the percent space by percent time plane (curve 1); 2) a reference 
curve based on 10% exceedance frequency and a temporal-spatial variance ratio of 1.0 
(curve 2); and 3) a reference curve based on 10% exceedance frequency and a temporal- 
spatial variance derived from chlorophyll data (curve 3). 


When regulating populations that are distributed in both space and time, this simple 
concept for regulating noncompliance must be extended to account for the variability 
in each dimension. While there is some added complexity in the mathematics, the 
fundamental concept remains the same: That is, to set the criterion threshold at a 
certain distance above the mean so that exceedance of that threshold will be rare in 
a healthy population. In this case, the distance by which the threshold must exceed 
the mean is a function of both the spatial and temporal variance components as 
described below. 

To establish these criteria thresholds for populations with two components of vari¬ 
ance, assume the simple model: 

Yj(Sj) + a x + PiiSj) 

where: 

H is the desired mean level of chlorophyll (in log space) 
a, is a random term for variation over time with variance a 2 , 

is a random term for variation over space with variance o 2 p 
Yj(Sj) is a water quality constituent measured at time i and location Sy 

The variance of Xy is u 2 a + a 2 ^ = a 2 . The standard dev of x is is sqrt(er) = o. It is 
common to allow an overall 10% exceedance rate without declaring an assessment 
unit out of compliance. We would expect 10% of the x is to fall above /u + 1.2815*a 


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Figure 4.2. Hypothetical lognormal distribution that might be typical of Chlorophyll. 
The figure illustrates the relation of the geometric mean and the criterion threshold 
set at the 90th percentile. 


where 1.2815 is the 90th percentile of the standard normal distribution. Thus (assum¬ 
ing normality) a population with spatial and temporal variance characterized by o 2 a 
and o 2 ^ that has a mean that is 1.2815*a below the threshold criterion should have 
an exceedance rate of 10% over space and time. Note that the reference curve is 
determined by the ratio o 2 Jo 2 ^ and the distance in standard deviations of the mean 
from the threshold. The actual values of the variance components, the mean, and the 
threshold, are not important as long as the relationships hold. Thus as long as the 
variance ratio is consistent, and mean to threshold distance is a fixed number of stan¬ 
dard deviations, the same reference curve will serve for all seasons and regions. 

Letting chlorophyll observed in the decade of the 1960s serve as a reference popu¬ 
lation, the parameters in Table 4.1 can be used to construct this reference curve based 
on the variance ratio and the mean to threshold distance given in the table. The ratio 
° 2 a /° 2 ti * s computed as the ratio of the temporal variance term and the spatial vari¬ 
ance term. The mean to threshold distance is computed to be 1.2815a for all regions 
and seasons. Based on there parameters, a reference curve for chlorophyll can be 
derived (curve 3 , Figure 4.1). For comparison a reference curve based on a variance 
ratio of 1.0 (curve 2, Figure 4.1) and the standard CBP reference curve (curve 1, 
Figure 4.1) are also shown. 


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Table 4.1. Chlorophyll criteria derived by computing and upper threshold based on predicted 
means for mid-flowl960s chlorphyll data. 


Season 

Salinity 

Zone 

Mean 

Log(chl) 

GMmean 

(chi) 

Temporal 

Variance 

Spatial 

Variance 

Std 

Dev 

lOR(chl) 

Threshold 

Criterion 

log(chl) 

Threshold 

Criterion 

(chi) 

Spring 

OH 

0 7684 

5 87 

0 0233 

0 0658 

0 2985 

1.2594 

18 17 

Summer 

OH 

1 1693 

14 77 

0 0233 

0 0658 

0 2985 

1 6603 

45 74 

Spring 

MH 

0 4137 

2 59 

0 0233 

0 0658 

0 2985 

0 9047 

8 03 

Summer 

MH 

0 8626 

7.29 

0 0233 

0 0658 

0 2985 

1 3536 

22 58 

Spring 

PH 

0 1386 

1 38 

0 0233 

0 0658 

0 2985 

0 6296 

4 26 

Summer 

PH 

0 218 

1 65 

0 0233 

0 0658 

0 2985 

0 709 

5 12 


Relative to the standard reference curves, the curve based on the observed variance 
ratio for chlorophyll is more restrictive of events where large portions of the popu¬ 
lation are out of compliance. For example, the CBP standard reference (curve 1) 
would allow 40% of area to exceed the criterion threshold up to about 6% of the 
time. The proposed chlorophyll reference curve (curve 3) would restrict occurrences 
of 40% of area out of compliance to about 2% of the time. Conversely, the proposed 
curve (curve 3) allows a higher frequency of events where a small percentage of 
space in out of compliance. For example, 10% of space is allowed out of compliance 
36% of the time under the proposed curve and 27% of the time under the standard 
curve. 

While there is mathematical and statistical logic underpinning this proposed chloro¬ 
phyll reference curve, it is important to remember that it is based on parametric 
models and simplifying assumptions. It is recommended that validation exercises be 
performed to insure that the general shape of CFD curves generated from data 
collected in near reference conditions is approximated by the proposed curve. 

4.3 ACCOMMODATING SEASONALITY IN REFERENCE CURVES 

The degree of acceptable exceedance can vary with season. For example, benthos are 
less tolerant of hypoxia in warmer water temperatures. In addition, the threshold 
criterion may never be exceeded in some seasons and frequently be exceeded in 
others. By combining seasons, the acuteness of a specific seasonal exceedence is 
diluted by data from the acceptable season(s). To some extent, seasonal differences 
can be accommodated by changing the threshold criterion among seasons. However, 
there may still be a need to develop separate reference curves by season. 


5.0 REVIEW CFD STATISTICAL PROPERTIES 
INCLUDING BIAS, PRECISION, AND INFERENCE 

The CFD as an assessment tool is a relatively new and unstudied concept. Its close 
relationship to the empirical distribution function does give some insight on the 
mathematical behavior of the CFD. In this section we review some of the properties 


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A-35 


of the CFD and discuss the complications that arise from these properties when the 
CFD is used as an assessment tool. After defining the population which determines 
the CFD. we go on to discuss the currently proposed sampling and estimation 
scheme, sources of error in the estimation scheme, and problems that result from 
these. The goal is to succinctly define these problems and elucidate possible solu¬ 
tions. This section will cover: the behavior of the CFD as a function of temporal and 
spatial variance, methods for construction CFD reference curves, the influence of 
sampling and estimation variance on the CFD shape, and feasible methods for devel¬ 
oping statistical inference tools. 

5.1 REVIEW OF CFD PROPERTIES 

With any statistical application, it is important to distinguish between the true 
descriptive model underlying the population being sampled and the estimate of this 
model derived from the data collected in a sample. As described above, the CFD has 
a data driven definition where the CFD is constructed based on a sample from a 
population for some water quality parameter. This population is a continuous 
random process over space and time. 

In order to quantify the statistical properties of the CFD, the CFD is defined in terms 
of a population of experimental units. This approach is a discrete approximation of 
the continuous random process in both time and space. However, the estimation 
scheme involves interpolation to discrete units in a spatial dimension and discrete 
days in the temporal dimension. To facilitate an understanding of the relation of the 
estimator to the true population, it seems reasonable to use a discrete approximation 
as the model for the true population. 

5.2 DEFINING THE CFD IDEAL 

The population will be defined as having different sizes of experimental units in 
much the way we think of a population that gives rise to a nested design or repeated 
measures design. The Chesapeake Bay will be partitioned into segments. Assessment 
will be done for each segment based on a three year record of the segment. Thus a 
three year period for the segment defines the entire population that will be parti¬ 
tioned into experimental units. The continuous time dimension is partitioned into 
days to form the primary units which are the state of a segment for a day. Call this a 
Segment-Day. Let there be M segment-days in the assessment period (typically 3 x 
365). The continuous spatial dimension is partitioned into N 3-dimensional cells 
(may range from hundreds to thousands). The state of each cell for a day will be a 
unit nested within the segment-day. The attribute of interest will be a measure of 
water quality for each cell for a day. Examples might be the mean level of Chloro- 
phyll-a in the cell for one day or the minimum of dissolved oxygen in the cell during 
the day. Let Y be a random variable for the attribute of interest and consider the 
following model 

Yj(sj) =n + +/?j(Sj) Eqn 5.1.1.1 


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A-36 


the vector a will be assumed to have expectation 0 and variance ^ a an< J 
each vector ^ will be assumed to have expectation 0 and variance 
i is the ordinal index for days and 
s is a vector valued ordinal for spatial location. 

Under this model, defines the correlation over time at the segment-day level and 
2/Ji defines correlation over space that occurs cell to cell within a day. 

Let C j(Sj) be a collection of threshold limits that define the acceptable criterion for 
the measured attribute. If Y j(sj) exceeds C j($j) in a cell, that cell is called degraded. 
The criterion is allowed to vary in both time and space so that in theory each Y j(Sj) 
might be compared to a unique C j(Sj).. It may vary over time because different 
levels of Y may be acceptable in different seasons. It may vary over space because 
different levels of Y may be acceptable in different salinity regimes so that even 
within a segment, C may be a function of salinity. As a rule, it is anticipated that 
C j(s'j) will be constant for regions of space and time such as salinity zones and 
seasons. 


Now convert the measured attribute Y j(Sj) to a Boolean response as follows 

TY i(sj) = I(Y jOj) > C j(Sj)) = 1 if Y j(Sj) > C s (Sj) Eqn 5.1.1.2 

= 0 otherwise 


Thus TY takes the value 1 when Y exceeds the threshold defined by C. Using TY, 
we summarize the state of a segment on one day as the fraction of that segment that 
is out of compliance 


Pi = (l/N)2” i TY l (s i ) 


Eqn 5.1.1.3 


The CFD that we wish to estimate is one minus the cumulative distribution function 
of the Pj’s. If P {i) represents the ordered values of the P,’s for any assessment period, 
then let 


G(p)-(l/M)]T i=| I(P,i)3p) Eqn 5 l lA 

G defines the CFD that if it were known would be used for an exact assessment. The 
cumulative distribution function is determined by the mean and variance of the ideal 
population. This population is defined with a spatial variance component and a 
temporal variance component. The final CFD shows the cumulative percent of time 
that a certain percent of space is below the criterion threshold. If the CFD shows that 
water quality in a segment is beyond the threshold for too much space and too much 
time, then the segment is classified as impaired. 

For one assessment period, G can be considered exact as defined above, but recog¬ 
nize that even this is only one observation of the many possible observations of G 
that could result from sampling different assessment periods. 

Assume for simplicity that Y is normal. If were 0 so that Y had constant expec¬ 
tation over time and if were of the form a 2 1 then each cell on each day would 
have constant probability of exceeding a constant value of C given by 1 - <J>(C) 


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where <T> is the normal cumulative density function. In this greatly simplified 
scenario, Pj would be the outcome of N independent Bernoulli trials. The ideal CFD 
would be the cumulative distribution function of M outcomes of a binomial random 
variable with N trials. If we allow ^ to have positive off diagonal elements, then the 
Bernoulli trials become dependent (i.e. adjacent cells are more likely to either both 
exceed or both meet the standard than distant cells). This should make the distribu¬ 
tion of the Pj more variable than under the independent binomial model, but the 
expectation of Pj would be constant over time. If we relax the assumption that 
is 0, then the expectation of the Pj would vary over time which would increase the 
variability of the Pj even more. 

Under the simplifying assumptions of independence, constant mean, and constant 
variance, it is possible to obtain an analytical formulation for the CFD based on the 
parameters of Eqn 5.1.1.1, However, when the more realistic time dependent, space 
dependent model with seasonal nonstationarity is considered, an analytical formula¬ 
tion is not tractable. The lack of an analytical formulation for this estimator under 
realistic dependence assumptions, e.g. non-trivial and points toward com¬ 
puter intensive simulation techniques to develop statistical inference procedures for 
this problem. None-the-less, it is interesting to consider the behavior of the CFD 
under the simplified model. 

5.3 CFD BEHAVIOR UNDER A SIMPLIFIED MODEL 

In what follows, the behavior of the CFD under various parameter formulations for 
Equation 5.1.1.1 are presented in graphical form. There are four parameters involved: 
p the population mean, <7 t the temporal variance, a s the spatial variance, and C the 
criterion threshold. In the examples that follow, three of these parameters are held 
constant and the fourth is varied to illustrate the effect of the varied parameter. 

In this exercise, the parameters of Equation 5.1.1.1 are simplified as follows: = o t 

I and ^ = <7 S I, where I is the identity matrix. Thus in both the temporal and spatial 
dimensions, independence and constant variance is assumed. 


Example 1. Example 1 considers the effect of changing the population mean on the 
shape of the CFD. 


Table 5.1. Parameter values and key for the family of curves shown in Figure 5.1. 


F 


<T S 

c 

color 

curve 

number 

5 

i 

1 

5 

Red 

1 

4 

i 

1 

5 

Orange 

2 

3 

i 

1 

5 

Brown 

3 

2 

i 

1 

5 

Green 

4 

1 

i 

1 

5 

Blue 

5 


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Pru|»rtiaii of 


Figure 5.1. A family of curves illustrating the behavior of the CFD as the population mean 
decreases from the criterion threshold. The parameter values for each curve and the 
corresponding curve number are given in Table 5.1. 


Note that when the population mean is equal to the criterion threshold, the CFD is a 
diagonal line from upper left to lower right (Figure 5.1, curve 1). This is largely an 
artifact of using symmetric distributions, the normal, for both the time and space 
variance components. That is, when the population median is equal to the criterion 
threshold, we expect an average of 50% noncompliance over time and we expect the 
exceed 50% noncompliance 50% of the time. 

As the overall population mean decreases from the criterion threshold, the family of 
curves tends to move from the diagonal line toward the lower left comer. Thus a 
reference population, which should have a small probability of exceeding the crite¬ 
rion threshold might have a shape similar to the green curve. This illustrates the 
importance of the shape of the CFD in measuring compliance. A CFD from a highly 
compliant population will tend to hug to lower left comer similar to the blue and 
green curves. As the population mean approaches the criterion threshold, the CFD 
approaches curve 1. If the population mean were to exceed the criterion threshold, 
the CFD would tend toward the upper right corner. 


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Example 2. Example 2 considers the effect of changing the temporal variance on the 
shape of the CFD. Note that the population mean is held constant at 3 which corre¬ 
sponds to curve 2 of the preceding example. 


A-39 


Table 5.2. Parameter values and key for the family of curves shown in Figure 5.2 


A 


°s 

c 

color 

curve 

number 

~T~ 

~T~ 

1 

S 

Red 

~r 



1 

5 

Orange 

~T~ 


3 

1 

5 

Brown 

~T~ 

~T~ 


1 

5 

Green 



5 

~r 

5 

Blue 

5 



Figure 5.2. A family of curves illustrating the behavior of the CFD as the temporal popula¬ 
tion variance increases. The parameter values for each curve and the corresponding curve 
number are given in Table 5.2. Note that the curve 1 here has the same parameters as 
curve 2 in Figure 5.1. 


As temporal variance increases, the frequency of large proportions of space going 
out of compliance increases (Figure 5.2, lower right). Conversely, the frequency of 
small proportions of space out of compliance (i.e. large proportions of space being 
in compliance) decreases (Figure 5.2, upper left). That is, shifting the daily mean 
either down or up tends to shift the entire segment toward or away from compliance. 


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In preparing water clarity CFDs for reference areas defined by having successful 
SAV beds, it is not unusual to find a curve shape similar to Figure 5.2 orange or 
yellow curves. This pattern suggests that SAV is tolerant of ephemeral events of 
spatially broad degraded water clarity. If water clarity is persistently degraded over 
portions of the area, SAV may be impaired. 


Example 3. Example 3 considers the effect of changing the spatial variance on the 
shape of the CFD. Again the population mean is held constant at 3 which corre¬ 
sponds to the curve 2 of the first example. 


Table 5.3. Parameter values and key for the family of curves shown in Figure 5.3 


n 


d s 

C 

color 

curve 

number 

3 

1 

1 

5 

Red 

1 

3 

2 

1 

5 

Orange 

2 

3 

3 

1 

5 

Brown 

3 

3 

4 

1 

5 

Green 

4 

3 

5 

1 

5 

Blue 

5 



Pmixrt-tion of S|>ar** 


Figure 5.3. A family of curves illustrating the behavior of the CFD as the spatial popula¬ 
tion variance increases. The parameter values for each curve and the corresponding curve 
number are given in Table 5.3. 


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A-41 


Increasing the spatial variance results in a family of curves that is complementary to 
those that follow an increase in temporal variance. Increasing spatial variance results 
in a higher frequency of small proportions being out of compliance. It is not so much 
an all-or-nothing phenomenon. 


Example 4. Example 4 considers the effect of changing both temporal and spatial 
variance on the shape of the CFD. 


Table 5.4. Parameter values and key for the family of curves shown in Figure 5.4. 



CTt 


c 

color 

curve 

number 

3 

1 

1 

5 

Red 

1 

3 

2 

2 

5 

Orange 

2 

3 

3 

3 

5 

Brown 

3 

3 

4 

4 

5 

Green 

4 

3 

5 

5 

5 

Blue 

5 



Figure 5.4. A family of curves illustrating the behavior of the CFD as both temporal and 
spatial variance increases. The parameter values for each curve and the corresponding 
curve number are given in Table 5.4. 


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Increasing the spatial and temporal variance together has the opposite effect of 
decreasing the population mean. The CFD tends to move in a direction of noncom¬ 
pliance. Thus compliance as measured by the CFD depends on the relative values of 
the population mean, the temporal and spatial variance, and the criterion threshold. 
Increasing the population mean has the same effect as decreasing the criterion 
threshold. Increasing population variance has the same effect as increasing the mean 
or decreasing the criterion threshold. In a sense, the CFD is measuring the distance 
between the population mean and the criterion threshold in units of variance analo¬ 
gous to a simple t-test. A nuance introduced here that has no analogy in the t-test is 
that the ratio of spatial to temporal variance controls the symmetry of the curve. 

5.4 UNCERTAINTY AND BIAS 

In Section 5.1., it was shown that the shape of the CFD is a critical element to deter¬ 
mining compliance. Thus it is important that this shape be primarily determined by 
the state of compliance of a segment and not be influenced by factors not relating to 
the status of compliance. Because the CFD is constructed based on data that are a 
sample from the whole, it is clear that some uncertainty in the CFD will result. In 
addition, the CFD is a function of the empirical distribution function (EDF) of frac¬ 
tion of space in compliance. The shape of this EDF is determined by the mean and 
variance of the sample. Thus any factor, such as sample size, that affects the preci¬ 
sion of the fraction of space estimate, will affect the shape of the CFD. In this section 
we review the effect of noncompliance factors on the shape of the CFD. 

Sample Size and Shape 

As noted, because the CFD is a function of the EDF of estimates of “fraction of 
space”, any factor affecting the precision of the estimate of fraction of space in 
exceedance will affect the shape of the CFD. In particular, the number of samples 
used for each p-hat (% exceedence) will affect precision. For a given segment, this 
fraction will be estimated more accurately if twelve samples are used to form the 
interpolated surface rather than six. Because of unknown spatial dependence in the 
data, it is difficult to analytically quantify the magnitude of this sample size effect. 
Therefore simulation analysis was employed to address this issue. 

Numerous simulation tests were performed. These begin with a simulation of struc¬ 
turally simple data that have no temporal or seasonal trend and progress to simulated 
data that mimic the temporal and spatial structure of observed data. Because the 
results from this latter simulation are most relevant, these are the results that are 
presented and discussed. 

Simulation Experiment 

Simulated data were created to mimic the properties of surface chlorophyll in the 
Patuxent estuary. Data were created to fill a 5 by 60 cell grid which approximates 
the long and thin nature of an estuary. These data have mean zero and a spatial 
variance-covariance structure chosen to approximate the spatial variance-covariance 
structure of cruise-track chlorophyll observed in the Patuxent estuary. Thirty-six 


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grids of data were simulated to represent 36 months in a three year assessment 
period. The temporal and spatial trends were added to the simulated data by adding 
in means computed for each month and river kilometer during the period Jan 1, 1991 
to Dec 31, 1993. Simulated data were created using the “grf’ function of the Geosta- 
tistical Package “geoR" of the R-package. 

After the full population of data was simulated for 3 year assessment period, a 
sampling experiment was conducted to assess the effect of sample size on the shape 
of the CFD. First, as a benchmark, a CFD was computed using all of the simulated 
data. To simulate the effect of sampling, a sample of fixed size was randomly 
selected from each the 36 5x60 grids of data. Using these samples, kriging 
(krige.conv function of geoR) was used to populate each monthly grid with esti¬ 
mates. These estimated chlorophyll surfaces were used to compute an estimate of the 
CFD which was graphically compared to the benchmark (Figure 5.5). For a fixed 
sample size, the process was repeated until it was clear whether the differences 
between the benchmark CFD and the estimated CFDs were due to variance or bias. 
To assess the effect of sample size, the process was repeated for several sample sizes. 

The effect of sample size on the shape of the CFD is consistent with expectations 
based on the relation of the CFD to the empirical distribution function (Figure 5.5). 
As sample size decreases, the variance of the estimated values of fraction of space 
increases. This increase in variance results in the estimated CFD being to the left of 
the true curve for low values of fraction of space and to the right of the true curve 
for high values of fraction of space. This assessment has been repeated many times, 
varying the threshold criterion, systematic vs. random sampling, the level of vari¬ 
ability in the simulated data, and so on. This sample size effect persists for every case 
where realistic estimation is employed. 

Sampling Scale and Shape 

As shown above (Figures 5.2-5.4) the shape of the CFD is a function of the ratio of 
temporal and spatial variance. To the extent that the ratio of these variance compo¬ 
nents in the data represent the true state of nature, this is acceptable. However, under 
a model with strong spatial and temporal dependence, the ratio of these variance 
components might be influenced by the scale of sampling in the spatial and temporal 
dimensions. For example, samples collected far apart in time might reflect higher 
variance than samples collected close in time. If the ratio of temporal and spatial 
variance is influenced by the density of sampling in each dimension, then experi¬ 
mental design will have an effect on the asymmetry of the CFD estimate. 

5.5 CONFIDENCE BOUNDS AND STATISTICAL INFERENCE 

An investigation into the use of conditional simulation to obtain confidence bounds 
for the CFD showed that not only is this a promising technique for statistical infer¬ 
ence, but also has potential in correcting bias associated with sample size effects that 
has been identified as a central problem in implementing the CFD approach. 
Correcting the bias of the CFD due to the sample size effect is important in obtaining 
confidence bounds on the CFD that cover the true CFD for a segment. Because bias 
correction is an important first step, this aspect of the conditional simulation exper- 


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Figure 5.5. Illustration of the effect of sample size (n) on the shape of the CFD for sample sizes 10, 20, 40, and 80. 


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A-45 


iments will be discussed first. Conditional simulation will then be evaluated in its 
efficacy in obtaining confidence intervals. 

This section first outlines the basic concept of conditional simulation and provides 
an algorithm that employs conditional simulation to estimate confidence bounds for 
the CFD. The results of this experiment support the potential of conditional simula¬ 
tion for correcting the sample size bias. A heuristic discussion of the mechanism 
underlying this adjustment for sample size effect is presented with the hope of moti¬ 
vating additional analytical investigation of this effect. 

Conditional simulation (Journel, 1974; Gotway, 1994) is a geostastical term for 
simulating a population conditional on information observed in a sample. In the case 
of kriging, a sample from a spatial population is used to estimate the variogram and 
mean for the population. The conditional simulation procedure generates a field of 
simulated values conditioned on the estimated mean and variogram from the sample. 
To the extent that the estimated mean and variogram approximate the true mean and 
variogram and the assumed distribution is a reasonable model for the true distribu¬ 
tion, repeated simulations of this virtual population will represent the variability 
typical of the true population. It follows that statistics computed from the condition¬ 
ally simulated fields will represent the expected variability of statistics from the true 
distribution. The CFD is a graphical representation of ordered statistics of percent 
compliance over time and it is a reasonable to assume that repeated conditional 
simulations will lead to effective confidence bounds for the CFD. 

Conditional Simulation Methods 

In the computation of the CFD, conditional simulation is implemented at the inter¬ 
polation step for each month. Interpolation produces an estimate of the spatial 
surface of the target parameter. From that estimate of the surface is obtained an esti¬ 
mate of the percent of noncompliance. Using conditional simulation, the surface can 
be reconstructed 1000 times. From the 1000 simulated surfaces are computed 1000 
estimates of the proportion of noncompliance. When this is repeated for each month 
for say 36 months, the result is an array of 1000 sets of 36 values of the proportion 
of noncompliance. Each of the 1000 sets of 36 can then be ranked from largest to 
smallest to compute a CFD in the usual way which results in 1000 CFD estimates. 
The variability among these 1000 CFDs can be used to estimate confidence intervals. 

To evaluate this concept, the following simulation experiment was conducted 

1. The first step is to simulate a population that will be considered the “true” 
population for this exercise. A grid of dimensions 5x60 is populated using an 
exponential spatial variance model with variogram parameters set to 
(0.00625026, 2.67393446). These variogram parameters were estimated from 
Patuxent cruise track chlorophyll data. This grid is populated 36 times to repre¬ 
sent 36 months. The mean and variogram are held constant for the 36 
simulations to create a simplistic case with no seasonal or spatial trend. Using 
this set of data, the CFD is computed in the usual way and this is considered 
the “true” CFD. 


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2. A sample of size 40 is selected from each of the 36 simulations at random loca¬ 
tions on the grid. Ordinary kriging is used to estimate the spatial surface for 
each simulation and from these 36 estimates of the monthly spatial surfaces, a 
CFD is computed. This is called the ‘estimated’ CFD. 

3. For each of the kriged monthly surfaces, 1000 conditional surfaces are simu¬ 
lated based upon the mean and variogram estimated from the sample data. The 
Cholesky decomposition is used to reconstruct the covariance structure indi¬ 
cated by the estimated variogram. The conditionally simulated surfaces were 
processed to yield 1000 estimates of the proportion of noncompliance. The 
1000x36 noncompliance values are used to compute 1000 CFDs, which are 
called the population of “conditionally simulated” CFDs. 

4. Each “rank position” of the monthly ordered proportions of noncompliance 
has 1000 values in this simulated population. To assess variability in the simu¬ 
lated population, graphs of the miniumum, the 2.5th percentile, the 50th 
percentile, the 97.5th percentile, and the maximum at each rank position are 
plotted to illustrate a 95% confidence envelop for the CFD (Figure 5.6). 

To test this procedure under various conditions, this basic simulation exercise was 
repeated varying the sample size and adding temporal and spatial trend to the simu¬ 
lation of the “true” population to reflect conditions more similar to real populations. 

Conditional Simulation Results 

The results of this simulation exercise are presented graphically. In Figure 5.6 the 
line 1 represents the CFD computed for the true population computed from the orig¬ 
inal data. The line 2 is the estimated CFD computed from kriging estimates based on 
samples from the true population. The line 3 lines represent the min and max of the 
1000 conditionally simulated CFDs. The two line 4s represent the 2.5 and 97.5 
percentiles of the 1000 conditionally simulated CFDs, which is the proposed 95 
percent confidence interval. The line 5 is the median of the 1000 CFD curves. 

Bias Assessment 

The results in Figure 5.6 are unusual in several respects. First note that the line 2 
shows the typical sample size bias for the CFD as described above (n=40). Relative 
to the true CFD (line 1) the estimated CFD is below line 1 for half the curve and 
above line 1 for the remainder. The first unusual feature is that the distribution of the 
conditionally simulated CFD curves is not centered on estimated CFD. In fact the 
estimated CFD is not completely within the bounds (min, max) of the conditionally 
simulated population. A surprising feature is that the median of the simulated popu¬ 
lation tracks fairly well with the true CFD (line 1). It is clear that the simulated CFD 
population is estimating something other than what is estimated by the estimated 
CFD (line 2). At the same time, it appears that the median of the simulated popula¬ 
tion is a good estimator of the true CFD and the proposed confidence bands (line 3) 
is reasonable confidence envelop about the true CFD. 

What follows is a heuristic explanation for why CFD computed from conditional 
simulations might be a better estimator of the true CFD than a CFD computed from 


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Figure 5.6. Confidence bounds computed based on quantiles of fraction of space 
computed on conditionally simulated surface estimates using variogram estimates 
from data. The base simulation has spatial correlation and no spatial or temporal trend. 
Sample size is 40. 


the kriging estimator. Additional analyses test whether this property might hold in 
general or is an artifact of the simple conditions (no spatial or temporal trend) under 
which this experiment was performed. 

In prior discussions we have noted that the CFD is the inverse of the CDF of the 
population of p's where p is fraction of space out of compliance with the criterion 
threshold. It is the variance of the p's that determines the steepness of the CFD: the 
smaller the variance, the steeper the CFD. In real applications, estimates of the p's 
have two important variance components. One variance component comes from true 
variance over time in the parameter being assessed. Another variance component 
comes from imperfect estimates due to sampling variability. In the base simulation 
with no spatial or temporal trend in the data, it is this second source of variance that 
controls the shape of the CFD. 


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A-48 


Because the variance of the p's is critical to the shape of the CFD, consider the vari¬ 
ance of p's computed from three sources in the experiment outlined above: 1) the true 
data, 2) a krig estimate based on a sample from the true data, and 3) conditionally 
simulated data based on a krig estimate of 2). To enhance our understanding of this 
comparison, the variance of the p's are discussed for two cases for each source. The 
first case assumes complete independence in the base simulation and does not use 
interpolation to estimate proportion of area out of compliance. This simplification 
allows us to easily infer the behavior of the CFD using analytical methods. The 
second case introduces an unknown spatial dependence in the base simulation and 
uses interpolated data to estimate the proportion of area out of compliance. These 
additional complexities make it difficult to implement analytical inference but 
conclusions may still be inferred by analogy to the simple independent case. 

Consider the sequence of sources where the base simulations are generated under the 
simple constraints of constant mean, constant variance and the errors for each cell of 
the grid that are independent. For this case the exceedance probability is: 

p = 1 - <F((x s - p - C) / o) 
where : C is the criterion threshold, 

x s is the data at location s, 
p is the mean used in the simulation, 

(7«is the variance used in the simulation, and 

<J> is the standard normal Cumulative Distribution Function. 

The distribution of the true p's computed from all 300 cells of the 5x60 simulation 
grid would behave like that of a independent binomial with N=300 with a variance 
of (p(l-p)/300). From these independent data draw a sample of size 40. Using only 
the proportion of the sample that is out of compliance to estimate the p's, the distri¬ 
bution of the p's would be that of a independent binomial with N = 40 and variance 
(p(l-p)/40). Clearly the p's estimated from the sample of 40 have much larger vari¬ 
ance than p's from the base simulation with 300 cells. Thus the true CFD computed 
using data from 300 cells will be steeper than the sample CFD computed from 40 
data points. This pattern is illustrated by comparing the true CFD (line 1) and the 
estimated CFD (curve 2) in Figure 5.6. This increase in the variance of the p's due to 
small sample size is the kernel of the sample size problem with the CFD. Now 
consider the behavior of p's computed from conditional simulations based on the 
sample. Compute x and s as estimates of (D and (D from the sample of 40 in the usual 
way. The conditional simulation is done by populating the 5x60 grid with data from 
a normal distribution with mean x t and variance s 2 j. The exceedance probability for 
these simulated data for the i th month is 

p'i = 1 - <J>((xs s - x, - C)/s') 

where : xs s is simulated data at location s 

Tj is the estimated mean used in the conditional simulation, and 
Sj is the estimated standard deviation used in the conditional simulation. 

If the p' were constant over months, the variance of the p's estimated by conditional 
simulation would be (p'( l-p')/300). The sample size component of this variance has 
been standardized to 300 which is the same as the sample size component of the true 
p’s, but the variability of conditionally simulated p's will be greater than that of true 


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p's because estimates of x, and s 2 , will vary over months. The parameter p and it’s 
estimate p' will be close if x and s are close to (D and (D. In the simple case with 
constant mean and independent errors, the CFD estimated by conditional simulation 
will better approximate the true CFD because both are based on binomial distribu¬ 
tions with the same N and approximately the same p. 

Now consider the same sequence of distributions where the assumption of inde¬ 
pendence is relaxed and interpolation of the data is used to estimate the proportion 
of noncompliance. The introduction of spatial covariance in the base simulation 
changes distribution of the true p's to a dependent binomial. The dependent binomial 
will have variance similar to an independent binomial with N < 300. Sample size that 
approximates the variance of the dependent binomial is termed Nb. The variance of 
the p's estimated from spatially dependent data is approximated by (p(l-p)/Nb) 
where Nb < 300 and thus the CFD from the independent case will be steeper than 
from the dependent case. The degree to which Nb is less than N will depend on the 
strength of the spatial correlation. 

Next consider the effect of dependent data and interpolation on the distribution of the 
p’s. When we interpolate the sample of 40 onto the grid of 300, the interpolated 
surface is smooth relative to the original data (compare curves 1 and 4 in Figure 5.2). 
Because of this increased dependence in the krig estimates, the estimates of p 
computed from the interpolated data behave more like binomial data with N=Ns (the 
sample size) than like binomial data with N=Nb (the number of grid cells). Because 
Ns is smaller than Nb, the variance of the population of p's computed from interpo¬ 
lated data will be greater. The greater variance explains why curve 1 in Figure 5.6is 
much flatter than line 1. 

Finally consider the effect of conditional simulation on the distribution of the p's. 
When data are conditionally simulated and the mean and variogram estimated from 
the sampled data are accurate, then the character of the simulated data will be similar 
to that of the true data (compare the line 1 with line 3 in Figure 5.7). Like the simple 
independent case, the population of p's computed from the conditionally simulated 
data will have a binomial variance that is similar to a binomial with sample size Nb. 
The simulation experiment shows that the CFD computed from these conditionally 
simulated p's will have a shape similar to the true CFD. This effect is illustrated in 
Figure 5.6 where the median of the conditionally simulated CFDs (blue line) is more 
similar to the true CFD line 1 than is the CFD estimate based on kriging (red line). 
Additional analytical work is needed to formalize the heuristic concepts presented 
here, but this finding indicates a productive direction in developing statistical infer¬ 
ence procedures in the CFD approach. 

Confidence Intervals 

The most successful technique for computing confidence bounds for the CFD were 
obtained using conditional simulation based on kriging interpolation of the sample 
data. The 95% confidence bands (lines 2, Figure 5.6) are well centered over the true 
CFD (line 1) for the simplistic case where the true data have spatial dependence but 
no spatial or temporal trends. When these simplistic assumptions are relaxed (Figure 
5.8) and the true data are simulated to have spatial dependence and temporal and 


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Center Transect 



northing 


Figure 5.7. Simulated chlorophyll data, kriging estimates based on a sample of the 
simulated data, and conditionally simulated data where the simulation is conditioned 
on the data used obtain the kriging estimates. 


spatial trends similar to chlorophyll data from the Patuxent estuary, the confidence 
bands cover the true CFD in this case as well. Experiments that varied the sample 
size also produced confidence bands with good coverage. 

Additional evaluation of the confidence band procedure should include a series of 
confidence band coverage experiments to assess the true coverage rate in comparison 
to the nominal coverage rate (e.g., 95% in this example). This series of experiments 
should be conducted with simulated data where the simulations are designed to 
produce data with properties similar to the three primary assessment water quality 
parameters. 


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A-51 


CFD simulations 



Figure 5.8. Confidence bounds based on quantiles of fraction of space computed on 
conditionally simulated surface estimates using variogram estimates from data. 

The base simulation has spatial and temporal trend estimated from Patuxent data. 
Sample size is 40. 


6.0 FINDINGS —SCIENTIFIC ACCEPTANCE OF CFD 
COMPLIANCE APPROACH 

6.1. CFD APPROACH AS BEST AVAILABLE SCIENCE 

This report represents an initial expert review of the CFD compliance approach. In 
addition the panel undertook simulation tests on the effects of 1) sample densities in 
time and space, 2) varying levels of attainment, and 3) varying degrees of spatial and 
temporal covariance. Further, trials of spatial modeling on fixed station Chesapeake 
Bay water quality data were conducted to begin to evaluate spatial modeling proce¬ 
dures. Based upon review of underlying theory, initial statistical assessments, and 
implementation feasibility, the panel finds that the CFD approach currently repre¬ 
sents best available science in its application to water quality attainment 


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A-52 


determinations in the Chesapeake Bay. Using criteria for Best Science and Best 
Available Science developed by the American Fisheries Society and the Estuarine 
Research Federation (Sullivan et al. 2006), we list relevant attributes of the CFD 
approach (Table 6.1). 

The CFD builds on important statistical theory related to the cumulative distribution 
function and as such, its statistical properties can be simulated and deduced. We have 
also shown that it is feasible to construct confidence ellipses that support inferences 
related to threshold curves or other tests of spatial and temporal compliance. Work 
remains to be done in understanding fundamental properties of how the CFD repre¬ 
sents likely covariances of attainment in time and space and how temporal and 
spatial correlations interact with sample size effects. Further, more work is needed 
in analyzing biases across regions and designated use segments. The panel expects 
that a two-three year time frame of directed research and development will be 
required to identify and measure these sources of bias and imprecision in support of 
attainment determinations. 

Through simulations of the CFD approach, it is feasible to analyze bias and error for 
both temporal and spatial sources of attainment variability. In particular, conditional 
simulations merit additional investigation as a relatively unbiased approach for 
supporting statistical comparisons among CFD curves. Much work remains to be 
done in understanding fundamental properties of how the CFD represents likely 
covariances of attainment in time and space. Still, the panel finds the approach 
feasible: one which merits additional development, testing, and application. Indeed, 
the CFD approach is beginning to attract scientific and management attention 
outside the Chesapeake Bay community. 

As shown by analyses in previous sections, the approach can efficiently combine 
spatial and temporal data to support inferences on whether regions within the Chesa¬ 
peake Bay attain or exceed water quality standards. On the other hand, we recognize 
substantial bias and imprecision can occur due to small sample size, non-independ¬ 
ence in temporal trends, and inadequate spatial interpolations. More work is needed 
in analyzing these biases across regions and designated use segments. Further, the 
old saw of needing more samples cannot be ignored. In particular, the panel is opti¬ 
mistic in the application of continuous spatial data streams made available through 
the cruise-track monitoring program, and the promise of continuous temporal data 
through further deployment of remote sensing platforms in the Chesapeake Bay 
(CBOS web site, etc). These data sets will support greater precision and accuracy in 
both threshold and attainment determinations made through the CFD approach. 

In classifying the CFD approach as best available science, we seek to make several 
important distinctions (Table 6.1). First, the CFD approach is a scientifically based 
approach based upon its clear purpose, conceptual and design framework, empirical 
procedures, documentation, and intent to develop rigorous statistical and review 
procedures (Sullivan et al. 2006, Daubert v. Merrell Dow Pharmaceuticals, Inc., 
1993). That the approach permits evaluation of uncertainty also supports its classi¬ 
fication as best available science (Christman 2006). On the other hand, we do not 
believe that the CFD approach yet constitutes best science. Here, further analyses of 
underlying statistical properties of the approach (including sampling design and 


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A-53 


Table 6-1. Evaluation of CFD approach as Best Science or Best Available Science accord¬ 
ing to AFS/ERF "Defining and Implementing Best Available Science for Fisheries 
and Environmental Science, Policy, and Management" (Sullivan et al. 2006). 


Attribute 

Best 

Science 

Best 

Available 

Science 

Current State of Development of CFD 

Approach 

Clear Objective 

YES 

YES 

Using biological response standards, combine 
available water quality in time and space to determine 
levels of attainment of Bay segments. 

Conceptual 

Model 

YES 

YES 

1. Bay divided into functional classifications - 
“Designated Uses.” 

2. Reference curves establish biologically 
relevant threshold levels for attainment. 

3. CFD combines and weights equally temporal 
and spatial sources of water quality 
variability. 

Experimental 

Design 

NO 

YES 

1. Bay segments are quasi-stratified for water 
quality data collection. 

2. Stratification of water quality data by 
designated units does not yet occur. 

3. Seasonal assessment of water quality 
attainment through spatial interpolation and 
the CFD approach is feasible but incompletely 
developed. 

Statistical Rigor 

NO 

YES 

1. Procedures for quantifying uncertainty 
associated with sampling design, spatial 
interpolation and CFD approach are feasible 
but incompletely developed. 

2. Procedures for interpolating water quality data 
are feasible but incompletely developed, 
particularly for 3-D interpolations of 
dissolved oxygen. 

3. Procedures for testing inferences related to the 
CFD curve are feasible but incompletely 
developed. 

Clear 

Documentation 

YES 

YES 

CFD approach, water quality sampling design, and 
current interpolation procedures well documented in 
Chesapeake Bay Program Reports and on website. 

Peer Review 

NO 

YES 

1. CFD approach and sampling design upon 
which it is based has not been peer-reviewed 
in the scientific literature. 

2. This report comprises the first external review 
by scientists with statistical expertise. 

3. Grey literature reports produced by CBP 
received expert and stakeholder input. 


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A-54 


interpolation elements) and vetting by outside experts is needed. Indeed, although 
the CFD approach is beginning to get featured in scientific venues, it has not yet 
been reviewed as part of the scientific literature. The panel sees this as an overdue 
next step for necessary for its acceptance, further development, evaluation, and 
application. 

The panel contrasted the CFD approach with existing state and jurisdictional water 
quality criteria and attainment procedures that are based strictly upon the observed 
sample, where site selection is not based upon probability sampling, inferences are 
not based upon error structure, and monitoring does not involve a scientifically 
rational design. Indeed, standard practice for assessing compliance with water 
quality criteria throughout the US is to sample monthly at a fixed set of stations and 
make judgments about compliance strictly from those samples. Sampling stations 
are typically located for convenience (e.g., bridge overpasses), there is reluctance to 
re-evaluate and change location (so as to maintain a time series at a fixed point), and 
no consideration is given to representativeness of the sample for the space/time not 
sampled. Thus the previous method used by the Chesapeake Bay Program, similar to 
the approaches used in other states, was simply based on EPA assessment guidance 
in which all samples in a given spatial area were compiled and attainment was 
assumed as long as > 10% of the samples did not exceed the standard. In this past 
approach all samples were assumed to be fully representative of the specified space 
and time and were simply combined as if they were random samples from a uniform 
population. This approach was necessary at the time because the technology was not 
available for a more rigorous approach. But it neglected spatial and temporal patterns 
that are known to exist in the standards measures. The CFD approach was designed 
to better characterize those spatial and temporal patterns and weight samples 
according to the amount of space or time that they actually represent. 

6.2 THE CFD APPROACH AND PEER REVIEW 

The panel views the CFD approach as innovative, one that has general application in 
water quality attainment assessments, but scientific acceptance of the approach will 
require that it is subjected to more extensive and targeted peer-review in the scien¬ 
tific literature. Because the CFD is a regulatory tool, it is particularly important that 
the approach is effectively communicated to the scientific community at large, for 
general acceptance but more critically for the sustained research and development 
that the CFD, as a nascent approach, requires. As highlighted elsewhere, bias and 
imprecision that can occur due to small sample densities, non-independence in 
temporal trends, and inadequate spatial interpolations. Such work is novel and 
should elicit interest among biostatisticians as it addresses questions of both funda¬ 
mental and applied consequence. 

Although, continued working groups, involvement through STAC of expert biosta¬ 
tisticians, and related reports such as this one will remain important in scientific 
acceptance of the CFD approach, the panel recommends immediate attention in 
subjecting the CFD to traditional peer review. One or several review papers should 
be submitted by CFD principals that lay out the theory, general approach and lists 
emergent scientific issues to stimulate other scientists to begin to address such 
issues. Several such papers might be appropriate given potential interest by 


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A-55 


biostatisticians and environmental and regulatory scientists. Scientific interest will 
also be garnered by public and stakeholder interest. The CFD approach here presents 
a challenge as it is complex in explanation. Still with careful diagrams and examples, 
a brochure on the CFD approach should be extremely useful in getting uninitiated 
scientists and stakeholders on the same page. 

6.3. BIOLOGICAL REFERENCE CURVES 

The success of the CFD-based assessment will be dependent upon decision rules 
related to the biological reference curves. These curves represent desired segment- 
designated use water quality outcomes and reflect sources of acceptable natural 
variability. The reference and attainment curves follow the same general approach in 
derivation—water quality data collection, spatial interpolation, comparison to 
biologically-based water quality criteria, and combination of space-time attainment 
data through a CFD. Therefore, the biological reference curve allows for implemen¬ 
tation of threshold uncertainty as long as the reference curve is sampled similarly to 
the attainment curve. Bias and uncertainty are driven in CFD curves by sample 
densities in time and space. Therefore, we advise that similar sample densities are 
used in the derivation of attainment and reference curves. As this is not always 
feasible, analytical methods are needed in the future to equally weight sampling 
densities between attainment and reference curves. 

Conceptually, the CFD approach builds on the underlying view that water quality 
criteria are surrogates for Designated Uses (regions that define ecosystem function). 
Implicit is a bottom up model based upon eutrophication, which is expected to 
diminish the designated use. Reference curves represent thresholds related to the 
functioning of designated use regions. Therefore, choice of reference regions or 
periods and sampling design in developing reference curve is critical to the imple¬ 
mentation of a scientifically-rigorous CFD approach. Choice of such regions is 
beyond the scope of this review, but we emphasize several relevant statistical issues 
in developing reference curves in Section 4. 


7.0 RECOMMENDATIONS FOR FUTURE EVALUATION 

AND REFINEMENT OF THE 
CFD ASSESSMENT METHODOLOGY 

As part of its conclusions, the STAC CFD Review Panel identified critical remaining 
issues that need resolution in the near future. The following is a list of critical aspects 
of that needed research. These research tasks appear roughly in order of priority. 
However, it must be recognized that it is difficult to formulate as set of tasks that can 
proceed with complete independence. For example, research on task 1 may show 
that the ability to conditionally simulate the water quality surface is critical to 
resolving the sample size bias issue. This discovery might eliminate IDW as a choice 
of interpolation under task 3. The Panel has made significant progress on several of 
these research tasks and CBP is encouraged to implement continued study in a way 
that maintains the momentum established by this research group (Table 7.1.). 


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Table 7.1. Research Tasks, examples of specific subtasks, and suggested time frame for 
continued CFD research. 


Task 

Schedule 

1. Effects of Sampling Design on CFD Results 

2006-2008 

(a) Continue simulation work to evaluate CFD bias reduction 
via conditional simulation. 

(b) Investigate conditional simulation for interpolation 
methods other than kriging - this may lead to more simulation work. 

(c) Implement and apply interpolation with condition 
simulation on CBP data. 


2. Statistical inference framework for the CFD 

2006-2008 

(a) Implement and evaluate confidence interval procedures. 

(b) Conduct confidence interval coverage experiments. 

(c) Investigate confidence interval methods for non-kriging 
interpolation methods. 

(c) Implement and evaluate confidence interval procedures. 


3. Choice of Interpolation Method 

2006-2008 

(a) continue to investigate other more nonparametric 
interpolation methods (e.g. loess and splines). 

(b) implement a file system and software utilizing the “best” 
interpolation for CBP data. 

(b) compare interpolations and CFD's based on IDW and 
“best” method. 


4. Three-Dimensional Interpolation 

2007-2009 

(a) Implement 2-D kriging in layers to compare to current 
approach of 2-D IDW in layers. 

(b) Conduct studies of 3-D anisotrophy in CBP data. 

(c) Investigate software for full 3-D interpolation. Examples 
of options include: custom IDW software, custom kriging software 
using GMS routines, custom kriging software using the R-package, or 
some other off the shelf product. 


5. High Density Temporal Data 

2008-2010 

(a) Develop methods to use these data to improve temporal 
aspect of CFD in current implementation. 

(b) Investigate feasibility of 4-dimensional interpolation. 



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A-57 


1. Effects of Sampling Design on CFD Results. The CFD is a special case of an 
unbiased estimator for a cumulative distribution function of a population. Like 
the cumulative distribution function, the CFD is a function of the mean and the 
variance of the population being assessed. And the better the mean and vari¬ 
ance are characterized with sample data, the more accurate the shape of the 
CFD will be. As the sampling density increases, the estimated CFD begins to 
approach the true CFD. However, if the sampling density is low, then sampling 
error could become important and there is potential that it could affect the 
shape of the CFD and ultimately the accuracy of the compliance assessment. 
Furthermore the potential for the sample size to affect the shape could create a 
compliance assessment bias if the reference curve and assessment curve are 
based on different sampling densities. Conditional simulation methods devel¬ 
oped by STAC panel members showed promise toward resolving these issues 
and mitigating potential biases caused by differences in sample size. 

2. Statistical inference framework for the CFD. It is important in a regulatory 
process to differentiate an exceedance that is small and might have resulted 
from chance variability from those that are large and indicative of an inherent 
problem. This differentiation will require mathematical tools to quantify the 
variability in the CFD that occurs as a result of sampling. The STAC panel 
made progress on this issue by demonstrating a confidence interval procedure 
based on conditional simulation associated with kriging. It remains to be 
assessed whether or not confidence intervals produced by this algorithm 
perform at the nominal level of coverage, fore example, does a nominally 95% 
CFD confidence interval cover the true CFD 95% of the time. 

3. Choice of Interpolation Method. The STAC panel considered several inter¬ 
polation methods and outlined the features of each. Those features illustrate 
tradeoffs between ease of implementation and maximizing the information 
garnered from the data. Further work is needed to compare the features to the 
requirements of wide-scale implementation of assessment procedures and 
formulate a plan for tractable implementation that results in credible assess¬ 
ments. One strategy is to implement easily performed analysis (e.g. IDW) as a 
screening tool to identify cases where compliance / non-compliance is clear, 
and then implement more labor intensive methods (e.g. kriging) for cases 
where compliance is more difficult to resolve. One difficulty with imple¬ 
menting a full comparison of methods is that implementation of each method 
requires considerable work in terms of setting up file systems, interfacing soft¬ 
ware and data, and coupling the considerable bathymetry data of the bay. Thus 
it would be prudent to narrow the choices based on theoretical considerations 
where possible. 

4. Three-Dimensional Interpolation. Assessments of the dissolved oxygen 
criteria require three-dimensional interpolation. However, the field of three- 
dimensional interpolation is not as highly developed as that of two-dimensional 
interpolation. While the mathematics of each method extend easily to three 
dimensions, there are relatively few examples of 3-D interpolation available in 
the literature and issues such as data density requirements for reliable results 
are not well studied. Efforts are needed to further evaluate research in three- 
dimensional interpolation and seek additional outside scientific input and 


appendix a • The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


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review with the goal of implementing the best available technology for this 
aspect of criteria assessment. One of the first efforts under this task is a study 
of the 3-D variance stucture of the data to be interpolated. A short term option 
is to implement the optimal 2-D interpolator in layers as is done with the 
current IDW interpolator. 

5. High Density Temporal Data. As currently formulated, assessment for most 
of the open-waters of the Bay are based on “snapshots” in time of the spatial 
extent of criteria exceedence estimated via interpolation. Data collected for use 
in interpolation are actually spaced over multiple days due to the large expanse 
over which sampling must be conducted. It is clear that technology is becoming 
available that will produce high density data in both space and time. Interpola¬ 
tion should accommodate data that are collected densely in space. However, it 
is unclear how the CFD process will accommodate data that are high density in 
time. Further work is needed to evaluate methods to fully utilize the temporally 
intensive data that is currently being collected. 

The panel discussed several mechanisms for the CBP to make progress on chal¬ 
lenging tasks ahead (Table 7.1). We recommend that a review panel oversee the tasks 
over the next 3-5 year time frame. This panel would periodically review trials and 
other products conducted by individual external scientists (academic scientists or 
consultants) and existing teams of CBP scientists (e.g., the Criteria Assessment 
Protocols (CAP) workgroup). Tasks 1 and 2 are most immediate and critical and we 
recommend that these tasks by contracted out to external scientists, exploiting state- 
of-the-art approaches and knowledge. Task 3 could be conducted through CAP or 
other group of CBP scientists. Task 4 and 5 are less immediate but again will require 
substantial expertise and innovation and may be most efficiently accomplished by 
scientific expertise outside the immediate CBP community. 


REFERENCES 

Christensen OF, Diggle PJ, Ribeiro PJ. 2001. Analysing positive-valued spatial data: the 
transformed Gaussian model. In: Monestiez P, Allard D, and Froidevaux, editors. GeoENV 
III - Geostatistics for environmental applications. Quantitative Geology and Geostatislics. 
Dordrecht (Netherlands): Kluwer Academic Publishers. 11:287-298. 

Christman MC. 2006. The characterization and incorporation of uncertainty in fisheries 
management, In Fisheries Ecosystem Planning for Chesapeake Bay. Bethesda (MD): Amer¬ 
ican Fisheries Society. (In press). 

Cressie N. 1989. The Many Faces of Spatial Prediction. Mathematical Geology 1:163- 176. 

Cressie N. 1991. Statistics for Spatial Data. New York: Wiley. 928 p. 

Curriero FC. 2006. On the Use of Non-Euclidean Distance Measures in Geostatistics. Math¬ 
ematical Geology (in press). 

Daubert v. Merrell Dow Pharmaceuticals. Inc. 1993. 509 U. S. Supreme Court. 579. 

Deutsch CV. 1984. Kriging with Strings of Data. Mathematical Geology. 26:623-638. 

Diggle PJ, Tawn JA, Moyeed RA. 1998. Model Based Geostatistics (with Discussion). 
Applied Statistics 47:299-350. 

Diggle PJ, Ribeiro PJ. 2006. Model-based Geostatistics. New York: Springer. 230 p. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 



A-59 


Dilie JA. 2003. How good is your weed map? A comparison of spatial interpolators. Weed 
Science 51:44-55. 

Gotway, CA. 1994. The Use of Conditional Simulation in Nuclear Waste-Site Performance 
Assessment. Technomctrics 36:2:129-141. 

Hastie TJ, Tibshirani RJ. 1990. Generalized Additive Models. New York: Chapman and Hall 
p335. 

Jensen OP, Christman MC, Miller TJ. 2006. Landscape-based geostatistics: A case study of 
the distribution of blue crab in Chesapeake Bay. Environmetrics 17:605-621. 

Joumel A. 1974. Geostatistics for conditional simulation of ore bodies. Economic Geology 
69:673-687. 

Kitanidis PK. 1997. Introduction to Geostatistics: Applications in Hydrogeology. New York: 
Cambridge University Press. 271 p. 

Kravchenko AN. 2003. Influence of Spatial Structure on Accuracy of Interpolation Methods. 
Soil Sci. Soc. Am. J. 67:1564-1571. 

Kutner MH. Nachtsheim CJ. Neter J, Li W. 2004. Applied Linear Statistical Models, 5 th 
edition. Boston: McGraw-Hill. 

Laslett GM. 1994. Kriging and splines: an empirical comparison of their predictive perform¬ 
ance in some applications. JASA 89:391-400. 

Lloyd CD. 2005. Assessing the effect of integrating elevation data into the estimation of 
monthly precipitation in Great Britain. Journal of Hydrology 308:128-150. 

Ouyang Y, Zhang JE, Ou LT. 2006. Temporal and spatial distributions of sediment total 
organic carbon in an estuary river. J. Environmental Quality. 35:93-100 

Rcinstorf F.Binder M, Schirmer M, Grimm-Strele J, Walther W. 2005. Comparative assess¬ 
ment of regionalization methods of monitored atmospheric deposition loads. Atmospheric 
Environment 39:3661-3674. 

Ribeiro PJ, Diggle PJ. 2001. geoR: A package for geostatistical analysis. R News 1:2:15-18. 
Schabenbcrger O, Gotway CA. 2005. Statistical Methods for Spatial Data Analysis. 

Boca Raton, FL: Chapman and Hall/CRC Press. 512 p. 

Spokas K, Graff C, Morcet M, Aran C. 2003. Implications of the spatial variability of land¬ 
fill emission rates on geospatial analyses. Waste Management 23:599-607. 

Sullivan PJ, Acheson J, Angermeier PL, Faast T, Flemma J, Jones CM, Knudsen EE, Minello 
TJ, Secor DH, Wunderlich R, Zanatell BA. 2006. Defining and implementing best available 
science for fisheries and environmental science, policy and management. Bethesda, Md: 
American Fisheries Society and Port Republic, Md. Estuarine Research Federation. Port 
Republic, Maryland, (available: www.fisheries.org/AFSmontana/AFS.ERF.BestScicnce.pdf) 

Tomczak, M. 1998. Spatial interpolation and its uncertainty using automated anisotropic 
inverse distance weighting (IDW) - cross-validation/jackknife approach. J. Geog. Infor. and 
Decision Analysis 2:18-30. 

[EPA] Environmental Protection Agency (US). 2003. Technical support documentation for 
identification of Chesapeake Bay designated uses and attainability. Annapolis (MD): EPA. 
177 p. EPA 903-R-03-002. (Available: http://www.chesapeakebay.net/search/pubs.htm) 

Valley RD.Drake MT.Anderson CS. 2005. Evaluation of alternative interpolation techniques 
for the mapping of remotely-sensed submersed vegetation abundance. Aquatic Botany 81:13- 
25. 


appendix a 


The Cumulative Frequency Diagram Method for Determining Water Quality Attainment 


A-60 


Ver Hoef JM, Peterson, E, and Theobald, D, 2007, Spatial Statistical Models that Use Flow 
and Stream Distance, Environmental and Ecological Statistics (in press). 

Wahba, G. 1990. Spline Models for Observational Data. Philadelphia (PA): Society for 
Industrial and Applied Mathematics. 169 p. 

Wang XJ, Liu RM. 2005. Spatial analysis and eutrophication assessment for chlorophyll a in 
Taihu Lake. Environmental Monitoring and Assessment 101:167-174. 

Zimmerman D., Pavlik C, Ruggles A, Armstrong MP. 1999. An experimental comparison of 
ordinary and universal kriging and inverse distance weighting. Mathematical Geol. 31:375- 
390. 


appendix a 


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B-1 


appendix 

Detailed Chesapeake Bay Water 
Quality Criteria Assessment 
Methodology 


The methods in this appendix apply specifically to the evaluation of dissolved 
oxygen criteria. For water clarity criteria or chlorophyll a criteria evaluations, the 
individual methods are very similar to those described here. See chapters 5 and 6, 
respectively, for additional details. Chapter 7 also contains important information in 
using shallow-water data for criteria attainment assessment of all three parameters. 

Data come from the Chesapeake Bay Program's Chesapeake Information Manage¬ 
ment System (CIMS) database or through the CIMS partners’ networked databases. 
The parameters extracted include date, location, depth, salinity, temperature, and the 
water quality parameter under assessment. Data identified by the states, but collected 
from other than the Chesapeake Bay Water Quality Monitoring Program and Chesa¬ 
peake Bay Shallow-water Monitoring Program, are also obtained. These data must 
be of known and documented quality as described in Chapter 3. 

Once the data are compiled, they are assigned to a time period based on the sample 
date. Fixed-station data are normally collected during a monitoring cruise that covers 
the entire tidal Chesapeake Bay over several days. To provide a “snapshot” of water 
quality, however, the data collected within one cruise are considered contempora¬ 
neous to enable a single spatial interpolation. For information not associated with a 
cruise, such as state-supplied data, a cruise number is assigned representing the 
closest cruise in time to the collection of each data point. Co-located data points in 
the same cruise are averaged. 

The criteria assessment procedure requires evaluation over large areas rather than at 
distinct points. Spatial interpolation is carried out for each water quality criteria 
parameter for each cruise (see Appendix D for details on the Chesapeake Bay inter¬ 
polator and the interpolation process) with water clarity and chlorophyll a data 
interpolated in the two horizontal dimensions using inverse distance squared 
weighting and natural logarithm transformation. Dissolved oxygen data are first 


appendix b • Detailed Chesapeake Bay Water Quality Criteria Assessment Methodology 


B-2 


linearly interpolated vertically within each column of observed data beginning at 
0.5 meters below the water surface and continuing at one-meter intervals, without 
exceeding the deepest observation in that water column. Data at each depth is then 
interpolated horizontally using inverse distance squared weighting. Data regions 
were specified for each segment to prevent the interpolation algorithm from using 
data points in neighboring tidal tributaries (described in the section below and in 
detail in Appendix D). 

Some designated uses for dissolved oxygen during the summer in the Chesapeake 
Bay and its tidal tributaries and embayments are defined vertically to distinguish 
stable water layers with different criteria levels (U.S. EPA 2003a, 2003b). In areas 
and seasons for which vertical stratified criteria apply, the surface mixed layer (open 
water) is that layer above the pycnocline and, thus, exposed to the atmosphere. The 
transitional middle layer (deep water) is the layer between the upper and lower pycn¬ 
ocline boundaries. The lower layer (deep channel) is the water below the lower 
pycnocline boundary. Given that the pycnocline is dynamic and moves up and down 
with each monitoring cruise, the designated use of each interpolator grid cell must 
also be defined based on the data for each cruise. 

Temperature and salinity are used to calculate density; density, in turn, is used to 
calculate pycnocline boundaries. Density is calculated using the method described in 
Algorithms for Computation of Fundamental Properties of Seawater For each 
column of temperature and salinity data, the upper and lower pycnocline boundaries 
are determined by looking for the shallowest robust vertical change in density of 
0.1 kg/m 3 /m for the upper boundary and the deepest change of 0.2 kg/m Vm for the 
lower boundary. To be considered robust, the density gradient must not reverse direc¬ 
tion at the subsequent measurement and must also demonstrate a change in salinity 
of at least 0.1 psu per meter (not merely a change in temperature). Chapter 7 in U.S. 
EPA 2004, pages 85-87, documents the detailed method for determination of both 
the vertical density profile and the pycnocline. 

The depths to the upper pycnocline boundary (where detected) and the fraction of 
the water column below the lower boundary are interpolated in two dimensions. If 
no lower boundary was detected, then the fraction is set at zero. The depth to the 
upper pycnocline boundary tends to remain stable in the horizontal dimension, 
meaning that spatial definition of that boundary using interpolation generally works 
well. Interpolation of the lower boundary is more complicated because the results 
may conflict with the upper boundary definition or with the actual bathymetry of the 
Chesapeake Bay. Consequently, interpolation of the lower boundary is based on the 
fraction of water column depth. In this way, the constraints of the upper pycnocline 
boundary definition and the actual Bay bottom depth are imposed, eliminating errors 
related to boundary conflicts. 


'Endorsed by UNESCO/SCOR/ ICES/IAPSO Joint Panel on Oceanographic Tables and Standards and 
SCOR Working Group 51. N.P. Fofonoff, and R.C. Millard, Jr., 1983. UNESCO Technical Papers in 
Marine Science. Pans, France. No. 44, p. 53. 


appendix b 


Detailed Chesapeake Bay Water Quality Criteria Assessment Methodology 



B-3 


Criteria assessments are based on each component criterion’s specific averaging 
period. Assessments of attainment of the instantaneous minimum criteria are directly 
evaluated using the individual cruise interpolations. All 30-day mean criteria assess¬ 
ments rely on monthly averages of interpolated data sets. To calculate these 
averages, each interpolated cruise within a month is averaged on a point-by-point 
basis in matching interpolator grid cells. Generally, two cruises per month run 
through the warm season with one cruise per month during the cooler period. Spatial 
violation rates are calculated for each temporally aggregated interpolation in an 
assessment period. For example, the 12 monthly average interpolations representing 
the four summer months (June, July, August, September) over three years were used 
for a three-year summer open-water dissolved oxygen assessment. 

Cumulative frequency diagrams (CFD) are generated from the spatial violation rates 
for each assessed designated use, water quality parameter, criterion, and averaging 
period using the Weibull plotting position (rank/(n+l)). 

The assessment CFD is compared to a reference CFD to determine if unallowable 
exceedances of the criterion occur. The diagrams of both CFDs show three areas: 
non-exceedance (above the assessment curve), allowable exceedance (below both 
curves), and unallowable exceedance (below the assessment curve and above the 
reference curve). If the assessment CFD surpasses the reference CFD at any point, 
an unallowable exceedance exists. 

Reference CFDs are continuous or generally have many more points than assessment 
CFDs. This situation can lead to spurious unallowable exceedances even without 
individual points in the assessment CFD topping reference CFD levels. To address 
this problem, reference curves are evaluated only at the temporal axis points in the 
assessment curve (see Figure II-7 in Chapter 2). For non-continuous biological refer¬ 
ence curves, these points are interpolated from neighboring points. 

The trapezoidal rule is used to calculate the areas. This rule is a method of approxi¬ 
mate integration, which calculates the areas of discrete trapezoids that make up the 
area below a curve when summed. Since both the assessment and reference curves 
are piecewise linear, repeated application of the trapezoidal rule results in an exact, 
rather than approximate, value. 

For dissolved oxygen criteria assessed without reference curves, the assessment 
space is divided in two—non-exceedance and unallowable exceedance. 


LITERATURE CITED 

U.S. Environmental Protection Agency (U.S. EPA). 2003a. Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity' and Chlorophyll a for the Chesapeake Bay and its Tidal Trib¬ 
utaries. EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office Annapolis. MD. 


appendix b 


Detailed Chesapeake Bay Water Quality Criteria Assessment Methodology 



B-4 


U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a Chesapeake Bay and its Tidal Tributaries: 2004 
Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, Annapolis, MD. 


appendix b 


Detailed Chesapeake Bay Water Quality Criteria Assessment Methodology 


C-1 


appendix C 

Evaluation of Options for 
Spatial Interpolation 


Interpolation constitutes a critical element of CFD-based assessment methodology. 
It provides the spatial framework for data integration while allotting the appropriate 
weight to all data. The spatial framework consists of a grid made up of a network of 
cells that vary in size to cover the entire spatial domain. The size of the cells deter¬ 
mines the scale of the assessment; smaller and more numerous cells in a given area 
provide a more spatially detailed assessment. Estimates for all cells come from a 
spatial interpolation algorithm. 

To date, two spatial interpolation algorithms have been considered; inverse distance 
weighting (IDW) and kriging. In IDW, estimates of water quality levels are based on 
a weighted average derived from the closest measured data values. Weights depend 
upon the distance between the measurement point and the cell being estimated. Thus, 
measurements from the closest points are weighted most heavily and have the most 
influence. The second method is kriging—a well-known statistical form of spatial 
interpolation. The statistical details of kriging rest on ample research. This method, 
however, has not been used for water quality criteria. Both spatial algorithm methods 
can prove valuable for Chesapeake Bay water quality criteria assessment; one or 
both will likely be used in the future. Other methods (non-parametric regression 
methods such as Loess regression or cubic splines) are also available and could also 
be considered for future use. Further details on the IDW and kriging methods are 
provided below. 


SPATIAL INTERPOLATION NEEDS SPECIFIC TO CHESAPEAKE 
BAY WATER QUALITY CRITERIA ASSESSMENT 

The Chesapeake Bay water quality criteria were established using the spatial defini¬ 
tion of designated-use areas for the tidal waters of Chesapeake Bay (U.S. EPA 
2003a, 2003b). These spatial definitions, along with the characteristics of the Bay 
itself, present several challenges for spatial interpolation. For example, the Chesa¬ 
peake Bay shoreline is extremely complex with many small tidal tributaries, 
embayments, and inlets that occur at various scales throughout the water body. The 
small inlets present a challenge for spatial interpolation because they require 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-2 


extrapolation from measured areas into unmeasured areas, often around numerous 
bends and twists in a tidal river. Furthermore, they create the potential for interpo¬ 
lating from one tidal tributary to another, which may be inappropriate since tidal 
tributaries are often hydrodynamically independent. Most spatial interpolation algo¬ 
rithms operate in two dimensions in a relatively simple spatial domain. Thus, 
specific refinements need to be made for the algorithms used in Chesapeake Bay 
criteria assessment. 

The Chesapeake Bay dissolved oxygen criteria depend on designated-use areas— 
specific volumetric areas with both vertical and horizontal dimensions (U.S. EPA 
2003a, 2003b). Dissolved oxygen levels are naturally lower in bottom waters. There¬ 
fore, the designated-use areas were defined as vertically stratified layers to allow 
establishment of criteria levels that support the ecological communities residing in 
the lower depths of the Bay. Any spatial interpolation supporting dissolved oxygen 
criteria assessment must allow interpolation throughout the designated-use volumes 
in three dimensions. The IDW algorithm developed and used by the Chesapeake Bay 
Program was designed in this way and has been used consistently to provide 
baywide maps of dissolved oxygen concentrations (see Appendix D). Kriging, 
however, has not been used for three-dimensional interpolation in the Chesapeake 
Bay to date; in fact, only limited research has taken place to develop the capability 
of three-dimensional kriging for any purpose (STAC 2006). Thus, more research 
may be required for the use of kriging in the assessment of dissolved oxygen criteria. 

The complexity of the Chesapeake Bay shoreline presents several obstacles for 
spatial interpolation in Bay tidal waters, mostly related to interpolating across land 
area. Most spatial interpolation algorithms assume a relatively simple spatial domain 
(e.g., rectangular) and interpolation takes place without regard to direction. In 
contrast, the Chesapeake Bay (for example, see Figure III-1 in Chapter 3) displays 
tidal flow patterns that make some locations independent or virtually independent. 
For Bay water quality criteria assessment, therefore, the influence between some 
locations must be limited when interpolating spatially. The current Chesapeake Bay 
Program interpolator provides limits by using data regions in which the data used to 
estimate values in given locations are limited to certain areas (see Appendix D for 
additional details). Similar or alternative methods may be required to apply kriging 
broadly. 

As described above, the Chesapeake Bay Program collects two types of data for 
criteria assessment; these two data types supply information at different spatial 
scales. The fixed-station Chesapeake Bay Water Quality Monitoring Program 
collects data consistently for the entire Bay as well as its tidal tributaries and embay- 
ments. The Chesapeake Bay Shallow-water Monitoring Program offers much more 
detailed information within Bay tidal tributaries and across all shallow-water habi¬ 
tats. Given the different spatial scales of these two monitoring programs, it is 
unlikely that they can be used in the same interpolations. Thus, two separate inter¬ 
polation approaches—each designed for specific types of criteria attainment 
assessments—may prove necessary. 

Since the Chesapeake Bay water quality criteria and the CFD-based criteria assess¬ 
ment methodology were developed and published, interest has developed in creating 


appendix c 


Evaluation of Options for Spatial Interpolation 


C-3 


a statistical basis for decision-making using the CFD (see pages 164-165 in U.S. 
EPA 2003a). Such a basis would allow the incorporation of error analysis into the 
criteria attainment assessment methodology. It would also allow the differentiation 
of an assessment based on a well-characterized system from one that was poorly 
characterized. Estimates of interpolation error are important to develop such a statis¬ 
tical framework. Such estimates allow decision-making to be based on the number 
(density) of sampling locations and promote greater statistical certainty (i.e., greater 
sampling density) in the assessment. The current Chesapeake Bay interpolation algo¬ 
rithm does not yield spatial error estimates (Appendix D); however, kriging is a 
possible alternative algorithm that can provide spatial interpolation error (STAC 
2006). 

Chesapeake Bay spatial interpolation requires the potential for automation. For 
many reasons, the Chesapeake Bay Program must compute many interpolations 
quickly. In developing the attainment figures for the 2006 listing cycle, for example, 
the program performed a total of 2328 interpolations for the final criteria assessment 
analysis of the 95 water quality segments). During development of the methodology, 
these interpolations were carried out repeatedly. Also, water quality models are often 
used to evaluate the potential benefits of management actions with the generation of 
multiple scenarios. Management action success is often defined in terms of water 
quality criteria, with results evaluated similarly to the actual measurements. Given 
the large number of data sets, automating the criteria assessment methodology and 
spatial interpolations would likely prove necessary. The current Chesapeake Bay 
interpolator allows automation and has been used in this way (Appendix D). Kriging, 
however, is a more detailed analysis that requires multiple decisions along the way, 
is not conducive to automation, and may not necessarily remain consistent within 
and between jurisdictions. 


DATA USED TO ASSESS CHESAPEAKE BAY 
WATER QUALITY CRITERIA 

As stated, the Chesapeake Bay Program redesigned the tidal monitoring program 
specifically to support water quality criteria assessment. That redesign resulted in 
multiple monitoring program components, all of which address one or more of the 
objectives of the Chesapeake Bay Water Quality Monitoring Program. Two of the 
components that serve most of the current needs of criteria assessment include the 
Baywide Fixed-station Water Quality Monitoring Program and the Shallow-water 
Monitoring Program. These two long-term efforts will provide data useful at 
different scales. 

The fixed-station monitoring program began in the mid 1980s and was designed to 
provide data for assessing long-term trends at key sites throughout the Chesapeake 
Bay and its tidal tributaries (Chesapeake Bay Program 1989). The program collects 
water quality samples at more than 150 sites (Figure C-l), including 49 stations in 
the mainstem Chesapeake Bay and 96 stations in the tidal tributaries. The samples 
go to a network of laboratories for analysis, compiling data on 19 water quality 
parameters. Fixed-station monitoring cruises run on a monthly basis throughout 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-4 



most of the year, but occur two times a month 
during the summer. At each station, samples are 
collected at multiple depths depending on the 
location of the pycnocline. In addition, techni¬ 
cians collect water quality sensor data— 
including water temperature, salinity, and 
dissolved oxygen—along vertical profiles at 
regular intervals. 

The fixed-station network provides data to assess 
water quality in the mid-channel, open waters of 
the Bay mainstem as well as in the major tidal 
tributaries and embayments. The network does 
not assess conditions in the shallows since many 
of the stations were purposely located in the 
main channels and open tidal waters. 

The Chesapeake Bay Program recently began 
monitoring shallow-water habitats using a tech¬ 
nology known as DataFlow (see Chapter 7 for 
details). This new technology uses a system of 
shipboard water quality probes that measure 
spatial position, water depth, water temperature, 
salinity, dissolved oxygen, turbidity, and fluores¬ 
cence from a flow-through stream of water 
collected near the water surface. This system 
allows rapid data collection (approximately 
every 4 seconds) while the boat is traveling at 
speeds up to 20 knots. Due to the speed of data 
collection, each cruise provides extremely 
detailed data sets useful for assessing highly 
variable water quality conditions, such as those 
expected in the Bay’s shallow waters and small 
tidal tributaries. Thus, this monitoring program 
specifically assesses shallow waters (STAC 
2005). The spatial density of data collected by 
the DataFlow system allows spatial interpolation. 
The current Chesapeake Bay Program interpolation software is not designed for data 
of this density, however, so new methods of interpolation need to be developed. 


Figure C-1. The sites that make up the fixed-station 
network of the Chesapeake Bay Water Quality 
Monitoring Program. 

Source: Chesapeake Bay Program 1989. 


Due to the cost of the Shallow-water Monitoring Program, it cannot be implemented 
baywide concurrently. Rather, the program is being put into practice on a rotating 
basis, with the monitoring system deployed to selected assessment units long enough 
to evaluate attainment and then moved to another set of units (see Chapter 7 for 
further details). This set-up means that all shallow-water areas will not be assessed 
simultaneously, although a full assessment will take place over time. For example, 
the Maryland Department of Natural Resources’ Water Quality Mapping Program 
covered 14 Chesapeake Bay and tributary systems in 2005.These systems include the 
St. Mary’s, Patuxent, West, Rhode, South, Middle, Bush, Gunpowder, Chester, 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-5 


Eastern Bay, MilesAVye, Little Choptank, Chicamacomico, and Transquaking rivers. 
In Virginia, DataFlow data are available for the Piankatank, York, Pamunkey, and 
Mattaponi rivers. Chapter 7 discusses additional details on plans for monitoring 
shallow-water systems. 

Other alternative monitoring programs have been considered, but not fully imple¬ 
mented for criteria assessment. Beginning in 1990, chlorophyll a concentrations 
have been measured over the mainstem Chesapeake using aircraft remote sensing 
(Harding et al. 1992). Twenty-five to 30 flights per year took place during the most 
productive time periods. In addition, satellite remote sensing data have been consid¬ 
ered for evaluating chlorophyll a concentrations in the Bay (Harding et al. 2004) 
although no detailed evaluation of the feasibility has been completed. Water quality 
sensors and data loggers mounted on buoys have also been evaluated as the best 
means to assess high-frequency dissolved oxygen criteria. This option is expensive, 
however, and only a limited (but growing) number of buoy systems have been 
deployed to date (http://www.cbos.org). 


INTERPOLATION METHODS CURRENTLY 
USED FOR CHESAPEAKE BAY WATER QUALITY 
CRITERIA ASSESSMENT 

The current Chesapeake Bay Interpolator is a grid-based algorithm in which criteria 
measurement data are used to estimate values for all grid cells (see Appendix D for 
a detailed description). Estimates for cell locations are computed by interpolating the 
nearest “n” neighboring water quality measurements for which “n” is normally 4 but 
is adjustable. The interpolation uses an inverse distance weighted (IDW) algorithm 
in which the estimated value of each grid cell is based on the four nearest measure¬ 
ments. Each of the neighboring points is weighted by the inverse of the distance 
squared (i.e., 1 d‘ : ), however, so close neighbors have more influence than those 
farther away. 

The cell size in the Chesapeake Bay interpolation grid is 1 km (east-west) x 1 km 
(north-south) x 1 m (vertical), with columns of cells extending from the water 
surface to the Bay bottom representing the three-dimensional volume as a group of 
equal-sized cells. Each tidal tributary is represented by variously sized cells 
depending on the river’s geometry since the narrow upstream portions require 
smaller cells to model the dimensions accurately. Interpolator grid cells, however, 
remain the same size within individual segments. This designation results in a total 
of 51,839 cells by depth for the mainstem Chesapeake Bay (segments 
CB1TF-CB8PH), and a total of 238,669 cells by depth for all 78 segments making 
up the mainstem Chesapeake Bay and its tidal tributaries and embayments. 

The Chesapeake Bay interpolator is optimized to compute concentration values that 
closely reflect the physics of stratified water bodies such as the Bay. Water quality 
varies much more markedly vertically as opposed to horizontally. To accommodate 
this attribute, each column of data is interpolated vertically to the same depths as the 
centroids of the interpolator cells, (i.e. 0.5, 1.5, 2.5 meters, etc). The interpolator then 
interpolates only in the horizontal dimension. 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-6 


Up to four points are used for interpolation. If fewer than four points exist, interpo¬ 
lation is still carried out given at least one measured point. Without any measured 
data, a missing value (normally a -9) is calculated for that cell. A search radius filter 
limits the horizontal distance of monitoring data from the cell being computed. Data 
points outside the user-selected radius (normally 25,000 m or 25 km) are excluded 
from calculation. This filter ensures that only data near the location being interpo¬ 
lated are used. 

Segment and region filters have also been added. Segments are aggregations of the 
interpolator cells. For instance, eight segments make up the mainstem Chesapeake 
Bay (CB1TF, CB20H,...CB8PH). The tidal tributaries have 70 additional segments, 
created by the Chesapeake Bay Program's 2003 segmentation scheme (U.S. EPA 
2004, 2005). These segments divide the Bay into geographic areas with somewhat 
homogeneous environmental conditions. This segmentation also allows the reporting 
of results on a segment basis, revealing more localized changes compared to the 
whole Bay ecosystem. 

The region file identifies the geographic boundary that limits which monitoring 
station data are included in interpolation for a given segment (see Appendix D). The 
purpose of the data region is to select a subset of the monitoring data from the input 
data file and to use that subset for computing the values for each grid cell in a 
segment. Use of data regions ensures that the interpolator does not “reach across 
land” to obtain data from an adjacent tidal tributary—a process that would give erro¬ 
neous results. By using data regions, each segment of grid cells can be computed 
from its individual monitoring data subset. Each adjacent data region overlaps so that 
a continuous gradient—not a seam—exists across segment boundaries. Data regions 
for criteria assessment vary somewhat from the data regions in the standard interpo¬ 
lator. These new regions were developed to exclude tributary measurements from 
mainstem interpolations and to include additional observed data from Virginia. 


EVALUATION OF THE INVERSE DISTANCE 
WEIGHTING SPATIAL INTERPOLATION 
ALGORITHM FOR ASSESSING CHESAPEAKE BAY 
WATER QUALITY CRITERIA 

The current Chesapeake Bay interpolator is based on an IDW algorithm—a non- 
statistical spatial interpolator that uses observed data to calculate a weighted average 
(as a predicted value) for each location on the prediction grid (Appendix D). The 
method calculates the weight associated with a given observation as the inverse of 
the square of the distance between the prediction location and the observation. The 
IDW is a spatial interpolator; in general, such methods have provided good predic¬ 
tion maps (STAC 2006). Additionally, implementation is relatively simple since 
software exists to map IDW automatically. Further, the method does not require any 
decisions during an interpolation session. Commercial Geographic Information 
Systems (GIS) software contains IDW, requiring only GIS skills for application. 

The IDW algorithm has several advantages for use in Chesapeake Bay water quality 
criteria attainment assessment (STAC 2006). First, since it is non-statistical, the 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-7 


algorithm is not constrained by prior theoretical assumptions concerning error struc¬ 
ture. It is, therefore, simpler mathematically and can be adapted to interpolation in 
three dimensions (i.e., with depth). Second, due to its simplicity, IDW does not 
require operator decisions at interim steps. Thus, it is conducive to automation— 
running large numbers of interpolation without having to make decisions as part of 
the interpolation process. The algorithm is susceptible to problems with interpo¬ 
lating across land; however, methods exist to prevent such problems for Chesapeake 
Bay application (as described in previous sections and in detail in Appendix D). It 
can be applied at any scale, but is most appropriate for large scales where three- 
dimensional interpolation becomes a necessity and data collection sites may remain 
too dispersed to provide good estimates of error structure no matter which algorithm 
is used. 

In addition to its advantages, IDW also has a major disadvantage: it is not a statis¬ 
tical method. The method is a deterministic approach without any sampling or model 
error assumed or accounted for (STAC 2006). In addition, IDW does not account for 
potential spatial autocorrelation among the observations and, therefore, does not 
fully utilize the information contained within the data. No method exists to estimate 
either source of error associated with a set of predicted values when using IDW and 
it cannot be used as a basis for statistical decision-making using the CFD. Dedicated 
research could determine whether IDW could be made more statistically defensible. 


EVALUATION OF KRIGING AS A SPATIAL 
INTERPOLATION ALGORITHM FOR 
ASSESSING CHESAPEAKE BAY WATER QUALITY 

Kriging has been considered by the Chesapeake Bay Program as a principal alterna¬ 
tive algorithm for spatial interpolation in CFD water quality criteria assessment 
methodology. Kriging is a spatial interpolation technique that arose from geostatis¬ 
tics, a subfield of statistics that analyzes spatial data. Kriging and the field of 
geostatistics have been used in a wide variety of environmental applications and are 
generally accepted methods for statistically optimal spatial interpolations (Cressie 
1991, Schabenberger and Gotway 2004, Diggle and Ribeiro 2006). Kitanidis (1997), 
Wang and Liu (2005), and Ouyang et al. (2006) elaborate on the application of 
kriging in water-related research. References on kriging methodology, geostatistics, 
and their related statistical development can be found in Cressie (1991), Diggle et al. 
(1998), Schabenberger and Gotway (2004), and Diggle and Ribeiro (2006). 

Kriging can be formulated equivalently in terms of a general linear regression 
model: 

Y (s) = /3 0 -h 1 Xi(s) • • • + /? p X p (s) + e(s) Equation C-1 

with s representing a generic spatial location assumed to vary continuously over 
some domain of interest and Y (s) capturing the outcome of interest measured at s, 
X|(s), . . . ,X p (s) potential covariates indexed by location s and their associated 

regression effects /3j./? p . The uncertainty in this regression relationship is 

modeled with the random error term e(s) assumed to have zero mean and constant 


appendix c 


Evaluation of Options for Spatial Interpolation 



C-8 


variance. Spatial data, similar to the type sampled in Chesapeake Bay water quality 
criteria assessments, often exhibit a property known as (positive) spatial dependence; 
observations closer together are more similar than those further away. This property 
is accounted for in the model by allowing £'(s) to contain a spatial correlation 
structure. 

Common distributional assumptions on £(s) include normality and log normality, 
although kriging can be based on other statistical distributions and data transforma¬ 
tions. Functions of a specific mathematical type (positive definite) represent the 
spatial correlation in £(s) and are assumed isotropic (correlation depends only on 
distance) or anisotropic (correlation depends on both distance and direction). Vari- 
ograms constitute another special type of mathematical function—closely related to 
spatial correlation functions—that are more often used to represent spatial correla¬ 
tion. In this case, and in many kriging applications, variograms and spatial 
correlation functions provide equivalent representations of spatial structure. For con¬ 
sistency, only the term “variogram” is used here in discussions of spatial structure. 

In the literature, Equation C-l is referred to as a universal kriging model. When 
covariates (the X’s) don’t influence interpolation of Y, the right hand side of model 
(Equation C-l) contains only the constant term/3 0 . The resulting model is called the 
ordinary kriging model. When the spatial structure (variogram) for the model (Equa¬ 
tion C-l) is known, statistically optimal predictions for the variable Y at unsampled 
locations (outside of estimation of possible regression effects) can be derived using 
standard statistical principles. The optimality criteria result in spatial predictions that 
are linear in the data, statistically unbiased, and minimize mean squared prediction 
error—known as best linear unbiased predictions. The minimized mean squared 
prediction error is also a measure of prediction uncertainty. In practice, however, the 
spatial structure of the data remains unknown. The estimation of the spatial structure 
using the variogram function, therefore, is critical to kriging applications. 

To demonstrate let (y(sj), . . . v(s n )} represent a sample set of spatial data such as 
dissolved oxygen collected at a set of n spatial locations Sj, . . .s n . Assume this data 
set to be a realization of the ordinary kriging version of model. The primary step in 
kriging is variogram estimation with several methods available; the method of 
moments and statistical likelihood based are two of the more common. All of these 
methods are based on the sample data (y(Si), . . . y(s n )}. This process ends with a 
chosen variogram function and its parameter estimation, describing the shape and 
strength (rate of decay) of spatial correlation. A determination, also based on the 
sampled data, is made of whether the spatial structure is isotropic or anisotropic. The 
estimated variogram is then assumed known. Kriged interpolations and their inter¬ 
polated uncertainty at numerous locations are computationally straightforward to 
generate. 

The following describes some of the benefits and potential limitations of kriging for 
the Chesapeake Bay Program to use in criteria attainment assessment application 
(with some comparisons to the IDW approach of spatial interpolation outlined in the 
previous section). A primary benefit of kriging compared to IDW is that it is a statis¬ 
tical technique. Statistics (including kriging) can make inferences from sampled data 
even in the presence of uncertainty; the quantity and quality of the sample data are 


appendix c 


Evaluation of Options for Spatial Interpolation 


C-9 


reflected in these inferences. Kriging, however, is a less-than-routine type of analysis 
and requires statistical expertise to execute. The short description on variogram esti¬ 
mation above merely introduces this involved and often complicated step. 

Further issues regarding kriging and Chesapeake Bay Program applications are 
listed below. 

• Kriging is flexible; it is based on an estimate of the strength of spatial dependence 
in the data (variogram). Kriging can consider direction-dependent weighted inter¬ 
polations (anisotropy) and can include covariates (universal kriging) to influence 
interpolations—either simple trends in easting and northing coordinates or water- 
related measures such as salinity. 

• A key feature of kriging is that a measure of uncertainty (called the kriged 
prediction variance) is generated along with kriged interpolations. Research 
has started to propagate this interpolation uncertainty through the CFD. 

• Kriging can be applied in situations for which the data remain sparse (such as 
the Chesapeake Bay Water Quality Monitoring Program's fixed station data) or 
dense (such as the Chesapeake Bay Shallow-water Monitoring Program). 
Kriged and IDW spatial interpolations may very well produce near identical 
results for these two extreme scenarios. The kriging approach, however, 
provides a statistical model, the uncertainty of which is influenced by the quan¬ 
tity and quality of data. Interpolation uncertainty information is crucial for both 
sparsely and densely sampled networks. 

In comparison to IDW, kriging is more sophisticated, but requires greater expertise 
in implementation. Kriging is available in commercial statistical software and also 
in free open-source applications, such at the R Statistical Computing Environment. 
Use of the technique requires geostatistical expertise programming skills for these 
two software packages. Segment-by-segment variogram estimation and subsequent 
procedures would require substantial expert supervision and decision-making. 
Chesapeake Bay Program managers may very well view this as a limitation in using 
kriging for certain Chesapeake Bay Program activities, such as criteria assessments, 
applications that need automated spatial interpolations. Furthermore, for some 
Chesapeake Bay Program applications, the decision on criteria attainment is clearly 
not influenced to any substantial degree by the method of spatial interpolation 
because the water quality conditions remain far out of attainment. One possible 
strategy is using a mix of IDW and kriging in situations for which attainment was 
grossly exceeded or clearly met (IDW) versus borderline cases (kriging). Table C-l 
provides a comparison of the capabilities of assessments based on lumping data, 
spatial interpolation based on IDW, and spatial interpolation based on kriging. 


appendix c 


Evaluation of Options for Spatial Interpolation 


C-10 


Table C-1. Comparison of the capabilities of methods for interpreting data for Chesapeake 
Bay water quality criteria assessment. 


Attributes 

Sample-based 

IDW 

Kriging 

Provides Spatial Prediction 

Yes 

Yes 

Yes 

Provides Prediction 

Uncertainty 

No 

No 

Yes 

Uncertainty for CFD 

No 

No 

Yes 

Deal with Anisotropy 

No 

Possible, but 
not routine 

Yes 

Can include cruise track/ 
fly-over data 

No 

No 

Yes 

Feasibility of 3-dimensional 
interpolations 

No 

Yes 

Possible, but 
not routine 

Feasibility of mainstem- 
tributary interpolations 

No 

Yes 

Possible 

Inclusion of covariates to 
improve prediction 

No 

No 

Yes 

Predictions of non-linear 
functions of predicted 
attainment surfaces P(y>c) 

No 

No 

Yes 

Level of sophistication 

Lowest 

Low 

Very High 

Automation 

Yes 

Yes 

No 


Source: STAC 2006. 


LITERATURE CITED 

Chesapeake Bay Program. 1989. Chesapeake Bay Monitoring Program Atlas - Volume 1: 
Water Quality and Other Physiochemical Monitoring Programs. CBP/TRS 34/89 U.S. Envi¬ 
ronmental Protection Agency Chesapeake Bay Program Office, Annapolis, MD. 

Cressie, N. 1991. Statistics for Spatial Data. Wiley, New York, NY, 928 pp. 

Diggle, P.J., J.A. Tawn, and R.A. Moyeed. 1998. Model Based Geostatistics (with Discus¬ 
sion). Applied Statistics 47:299-350. 

Diggle, P.J. and P.J. Ribeiro. 2006. Model-based Geostatistics. Springer, New York, NY. 230 

pp. 

Harding, L.W., Jr., E.C. Itsweire, and W.E. Esaias. 1992. Determination of phytoplankton 
chlorophyll concentrations in the Chesapeake Bay with aircraft remote sensing. Remote 
Sensing of the Environment 40: 79-100. 

Harding, L.W., Jr., J.G. Kramer, and J. Phinney. 2004. Estuarine and Watershed Monitoring 
Using Remote Sensing Technology Present Status and Future Trends: A Workshop Report, 
7-8 January> 2002, Annapolis, Maryland. Scientific and Technical Advisory Committee and 
Maryland Sea Grant College. Maryland Sea Grant Publication UM-6-SG-TS-2004-03. 
College Park, MD. 

Kitanidis, P.K. 1997. Introduction to Geostatistics: Applications in Hydrogeology. Cambridge 
University Press, New York, NY, 271 pp. 

Ouyang Y, J.E. Zhang, and L.T. Ou. 2006. Temporal and spatial distributions of sediment 
total organic carbon in an estuary river. Journal of Environmental Quality 35:93-100. 

Schabcnberger, O and C.A. Gotway. 2005. Statistical Methods for Spatial Data Analysis. 
Chapmann and Hall/CRC Press, FL, 512 pp. 


appendix c 


Evaluation of Options for Spatial Interpolation 



















C-11 


Scientific and Technical Advisory Committee (STAC). 2006. The Cumulative Frequency 
Diagram Method for Determining Water Quality' Attainment: Report of the Chesapeake Bay 
Program STAC Panel to Review o Chesapeake Bay Analytical Tools STAC Publication 06- 
003. 9 October 2006. Chesapeake Bay Program Scientific and Technical Advisory 
Committee. Chesapeake Research Consortium. Edgewater, MD. 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity’ and Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office. Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Chesapeake 
Bay Designated Uses and Attainability. EPA 903-R-03-004. Region III Chesapeake Bay 
Program Office Annapolis, MD. 

U.S. Environmental Protection Agency. 2004. Chesapeake Bay Program Analytical Segmen¬ 
tation Scheme: Revisions, Decisions and Rationales 1983-2003. EPA 903-R-04-008. 
CBP/TRS 268/04. Region III Chesapeake Bay Program Office. Annapolis, MD. 

U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen¬ 
tation Scheme: Revisions, Decisions and Rationales 1983-2003: 2005 Addendum. EPA 
903-R-05-004. CBP/TRS 278-06. Region III Chesapeake Bay Program Office, Annapolis, 
MD. 

Wang, X.J. and R.M. Liu. 2005. Spatial analysis and eutrophication assessment for chloro¬ 
phyll a in Taihu Lake. Environmental Monitoring and Assessment 101:167-174. 


appendix c 


Evaluation of Options for Spatial Interpolation 


D-1 


a ppen 


dix Cl 


User Guide and 
Documentation for the 
Chesapeake Bay Interpolator 


INTRODUCTION 

The Chesapeake Bay and Tidal Tributary Interpolator computes water quality 
concentrations throughout the Chesapeake Bay and/or tributary rivers from water 
quality measured at point locations. The purpose of the Interpolator is in compute 
water quality concentrations at all locations in the 2-dimensional plane (top or 
bottom depth) or throughout the 3-dimensional water volume. Results of the inter¬ 
polation can then be compared over time to compute trends or individual 
interpolations can be overlaid with other data to visualize possible cause and effect 
relationships. One example is to compare water quality with living resource (fish, 
shellfish, aquatic vegetation) distributions. Results of the Chesapeake Bay Interpo¬ 
lator have been used since 1988 to determine trends in water quality for the 
Chesapeake Bay Program (http://www.chesapeakebay.net/). 

Version 4.2 of the VOL3D software includes new code to: 1) import data from 
Microsoft ACCESS data tables; 2) draw improved graphics of tributary segments; 3) 
draw colors using categories, as before, or to draw using a color ramp of 255 colors; 
4) draw longitudinal sections which represent the centerline of the Bay or Tributary 
River segments; 5) draw images of all Tributary Rivers in addition to the Bay; and, 
6) compute composite images that represent the minimums or maximums over a 
time series. 

Another tool, DART, which must be run on the CIMS network at the Chesapeake 
Bay Program Office, creates data sets for the Interpolator for any parameter in the 
historical water quality data base. DART is a very powerful tool which can create 
many data sets in a very short time. Anyone who needs to interpolate data held by 
the Bay Program, should investigate the use of DART. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 





































































D-2 


INTERPOLATOR DESCRIPTION 

The Chesapeake Bay Interpolator is a cell-based interpolator. Fixed cell locations are 
computed by interpolating the nearest n neighboring water quality measurements, 
where n is normally 4, but this number is adjustable. Cell size in Chesapeake Bay 
was chosen to be 1km (east-west) x 1km (north-south) x lm (vertical), with columns 
of cells extending from surface to the bottom of the water column, thus representing 
the 3-dimensional volume as a group of equal sized cells extending throughout the 
volume. The tributaries are represented by various sized cells depending on the 
geometry of the tributary, since the narrow upstream portions of the rivers require 
smaller cells to accurately model the river’s dimensions. This configuration results 
in a total of 51,839 cells by depth for the Main Bay (Segments CB1TF-CB8PH), and 
a total of 238,669 cells by depth for all 77 segments which comprise the Main Bay 
and tributaries. Computation time on a Pentium 2 ghz PC running Windows XP is 
approximately 15 seconds for the Bay and tributary interpolator model. 

The Chesapeake Bay Interpolator is unique in the way it computes values in 3- 
dimensions. The interpolator code is optimized to compute concentration values that 
closely reflect the physics of stratified water bodies, such as Chesapeake Bay. The 
Bay is very shallow compared to its width or length, hence water quality varies much 
more vertically than horizontally. The Chesapeake Bay Interpolator uses a vertical 
filter to select the vertical range of data that are used in each calculation. For 
instance, to compute a model cell value at 5m deep, monitoring data at 5m deep are 
preferred. If fewer than n (4) monitoring data values are found at the preferred depth, 
the depth window is widened to search up to cl (normally +/-2m) meters above and 
below the preferred depth, with the window being widened in 0.5m increments until 
n monitoring values have been found for the computation. The smallest acceptable n 
value is selectable by the user. If fewer than n values are located, a missing value 
(normally a -9) is calculated for that cell. 

A second search radius filter is implemented to limit the horizontal distance of moni¬ 
toring data from the cell being computed. Data points outside the radius selected by 
the user (normally 25,000m) are excluded from calculation. This filter is included so 
that only data that are near the location being interpolated are used. 

In this version of the Interpolator, Segment and Region filters have been added. 
Segments are geographic limits for the interpolator model. For instance, the Main 
Bay is composed of 8 segments (CB1TF, CB20H, ...,CB8PH). The tributaries are 
composed of 69 additional segments, using the CBP 1998 segmentation scheme 
(Figure D-l). These segments divide the Bay into geographic areas that have some¬ 
what homogeneous environmental conditions. This segmentation also provides a 
means for reporting results on a segment basis that can show more localized changes 
compared to the whole Bay ecosystem. To replicate the segmentation scheme, the 
segment boundaries were used to cookie-cutter out the Interpolator cells that fall 
within each segment. Each set of these cells are then identified inside the corre¬ 
sponding *.bth file that contains the bathymetry definitions. To compute the 
interpolated values for the Main Bay, the corresponding bathymetry file is named 
“cbay8.bth”. This file contains the cell locations for the cells in the Main Bay Inter¬ 
polator. A similar file, ‘ > bay_trib.bth” contains the cell definitions for the Main Bay 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


D-3 


MClRTF 


CB1TF 
8 SHO H 
GUNOH. 


LK'OH 


POTTF 


.IMSTF 

apptf 



POCTF 


ELIMH 


LYMPH 


S B EM H 


Figure D-1. Chesapeake Bay Program 1998 segmentation design. 


and tributary interpolator. Other .bth files have also been created for individual river 
systems. Users that need specialized processing, such as finer resolution or addi¬ 
tional segments in a particular area of interest, must create a new bathymetry file that 
defines the bathymetry of the area of interest at the desire cell-size. 

Regions filters (cbay8.drg, bay_trib.drg, etc) are files which contain a closed 
polygon of x-y points that define an area larger than the corresponding * .bth file. The 
region file identifies the geographic boundary that limits which monitoring station 
data are included in interpolation for a given segment. The purpose of the data region 
is to select a subset of the monitoring data from the input data file, and to then use 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 











D-4 



Pdioisetri I 0 j*a iaipoil 8 InteipoJatc 


bfdphici 


Choota D«l4>ais 


SAMPLE J)ATE 


i LAYER 


[STAflON 


QUALIFIER 


' ie»«w 


LON on (JOE 




t*Sr*> 


LATIIUt'L 


; ChUISE.I0 


legunad 


DEF'IH 


parameter 


[VALUE 


Data File Creation Tool 


CHESAPEAKE BAY PROGRAM INTERPOLATOR 


Rcpoili 


Figure D-2. Screen 1 includes seven navigation buttons and the Data 
File Creation Tool for importing data from an ACCESS data base and 
creating VOL3D data files.Once the user has selected the desired data 
fields in the data table (Figure D-3), the Data File Creation Tool opens a 
new screen that provides a range of options to the user for selecting 
and subsetting data from the ACCESS data table (Figure D-4). The Data 
Engine allows the user to select data by parameter, by date range, to 
set interpolation control parameters, to choose the desired bathymetry, 
to select data by depth ranges or layers, and finally to choose how the 
group the resulting data in one or more output files. The "Create Files" 
button, when pushed, will generate data files in the VOL3D ".d3d" file 
format. These files are then ready for interpolation. 


that subset for computing the values for each cell in a segment. Use of data regions 
ensures that the interpolator does not “reach across land” to obtain data from an adja¬ 
cent river which would give erroneous results. By using data regions, each segment 
of cells can be computed from their individual subset of monitoring data. Each adja¬ 
cent data region should overlap by some amount so that there is a continuous 
gradient, and not a seam, across segment boundaries. 

In the future, a pycnocline filter may be added to the Interpolator, so that water 
above, within, and below the pycnocline are not interpolated together. Since the 
water quality in various parts of the pycnocline can be so dramatically different, the 
Interpolator file structure will be modified to handle this requirement. 

INSTALLATION 

The Vol3D Interpolator code and auxiliary files have been bundled together into a 
SETUP application and then PKZipped to reduce the overall file size. The Vol3D.zip 
file must first be unzipped into a directory on any standard PC running the Windows 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 

































D-5 


95/98/XP operating system. Once unzipped, double click the SETUP.EXE file to 
start the installation process. It is suggested that the application be installed in the 
C:\VOL3D directory. The original zipped file can be deleted to regain disk space. A 
fast Pentium machine with 256 mb ram and 1 gb disk drive will prove useful. 


USING THE CHESAPEAKE BAY INTERPOLATOR 

PROCEDURE FOR USING THE VOL3D INTERPOLATOR 

Begin using the VOL3D software by double clicking the VOL3D.EXE icon on the PC. 

The first screen provides 7 buttons (Geography, Parameter, Data Import, Interpolate, 
Math, Graphics, and Reports) that step the user through the interpolation, graphics, 
and reporting process. Also on the first screen, is a Data File Creation Tool , that can 
be used to create VOL3D compatible data files from an external ACCESS data base. 
The ACCESS data base needs to contain data necessary for interpolation, as identi¬ 
fied on Figure D-2. Essential data fields include STATION, LONGITUDE, 
LATITUDE, DEPTH, and VALUE. Other fields, including SAMPLE_DATE, 
PARAMETER name, LAYER, Qualifier (<, >), CRUISE, and CRUISE_ID, provide 
data that can be used to select or subset the monitoring data by cruise, layer, or date. 



CHESAPEAKE BAY PROGRAM INTERPOLATOR 


Gf*ogi<tphy 


PiM<Mietet 


D«t« Import 


Report* 


Intel potMe 


Data File Creation Tool 


Choose Database §C \Vof3D4C?\dV*1993nvdb 


cbpivq*) 


$AMPL£_D,4T£ 


[STATION 


STATION 


QUALIFIER 


ILONGiTUOE 


LONG 


CRUISE 


PARAMETER 


Lie Me IrUeipolaloi 
Data Seis 


Figure D-3. Example of data fields selected from the "cbpwq99" data 
table in the "data1999.mdb" ACCESS data base. The Data File 
Creation Tool allows a user to extract data from an ACCESS table into 
desired data files for VOL3D 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


























D-6 


Volumetric interpolator Data Engine 


mm* 


I \ OS '. Mi I Rf( I\ ! ! R 

Ddiabase Name , r C \Vol30403\data1999 mdb 


T able Name r c bpwq99 


Enlet Descipltve Title 

[Chesapeake Bay Walei Quality Analysis 

Choose Paiametei [CHLA.ChlorophyT 


CHLA C>,io. 


DO.Dissolved Oxygen 
SALINITY.Sabrxty 
WTEMP.Watei Tempeiatuie 
NH4F Ammonia 
N02F Nitrite 

|N03FNitiate ' 

Choose Start Date ji / 5/1999 




1 /S/1939 
1/7/1999 
1/11/1999 
1/12/1999 

T lanslc.m 
• Lmeai 


Choose Geography 
(* All Bay and Tribs 
C Che« Bay ICB1C88) 

Dies Bay Mobiack Tangier 
Chester 
Choplank 
F Etaab*th 

Output File Name 


Inieipolatoi Settings 
M murium N eighbor s (( 

Maximum Neighbors ( 4 " ~ 

Horeontal Range (M) (25000 

Misang Value (!g 

Choose End Date f 1/5/1999 


Log (Base 2 ) 


r 


1/6/1999 

1/7/1999 

1/11/1999 

1/12/1999 


F 1/SqRooi 

James 

Naniicoke 

Patuxent 

Pocomoke 

Potomac 

Rappahannock 


York. 


Detection Limit 

Repoited Value 
F One-half Repoited Value 
F Fixed Value 

Choose Depth Range 

Depth Range (m| Top (q Bottom (50 

Depth Layeis 

f r 

r r 


Gioupmg of Data 
f* All data m single data set 
F Group by BAY_CRUISE 


Group by CruueJD (Yi*Month) 


r Group by Year Month F 

r 

F Group by Month 

Group by Season IMM/DOI 
P Spimg (03/21 


r 

r 


r 

F 


F 

F 


r 

r 


r Group by Year ♦ Season 
P Pa# [09/21 


(990105.085251 d3d 


Browse 


W Summer 06/21 


Deate Files 


W Winter ,12/21 


Done 


Figure D-4. The Data Engine screen provides many options for the user to aggregate data selected from an 
ACCESS data base table. Similar capabilities are built into the DART tool at the Chesapeake Bay Program, that 
allows access to the entire historical water quality data base. 


GEOGRAPHY BUTTON 

Click the Geography Button to select the Geography screen. Select the bathymetry 
that matches your requirement, such as, Chesapeake Bay or Bay and tidal tributaries 
(Figure D-5). 


PARAMETER BUTTON 

Click the Parameter Button to select the Parameter screen. Select the parameter 
that matches your requirement, such as, dissolved oxygen, salinity, or water temper¬ 
ature (Figure D-6). The number of significant digits of numerical precision is 
pre-selected for each parameter, but the value can be changed by the user in the 
Params.ini file. 


appendix d 


User Guido and Documentation for the Chesapeake Bay Interpolator 





































D-7 



Figure D-5. Geography screen. Select desired bathymetry. 



Data Impwl 


Interpolate 


Giephtcs 


RepoiU 


t*h 


cj 


v r&mwat 




CHESAPEAKE BAY PROGRAM INTERPOLATOR 


P diameter 




Figure D-6. Parameter screen. Choose desired parameter for analysis. 


appendix cj 


User Guide and Documentation for the Chesapeake Bay Interpolator 




































D-8 


DATA IMPORT BUTTON 

Click the Data Import Button to select the Data Import screen. On this screen, click 
the “Get File Name” button to select the data file that contains the data that are to be 
interpolated (Figure D-7). The default file extension for data files is .d3d. d3d files 
include the X and Y coordinates (UTM Zone 18, NAD83 is recommended for inter¬ 
polation. These are also required for the graphics tools.) 


My Recent 
Documents 


Desktop 

My Documents 

My Computer 

S! 

My Network 

Pieces 


Look m VoiSD 


BtXW7D^n,D3D 


m 

/JBD097C 716.030 
_']S0O9 70301.030 
i_»]B0O97D816.030 
800970901. DSD 


is a 


ftt name 
F»es of type 


|’e:OS737C1 D3D 




Open 


| Data Fte fd30 




Cancel 


Figure D-7. Data Import screen, "Get File Name" window. Select a data 
file (.d3d) for analysis. 


Once the file has been selected, the other fields on this screen will populate with 
information about the data file, including, start and end dates of the data, the number 
of observations, the date the file was created, the parameter name and code, and title 
(Figure D-8). Normally data do not need to be transformed, however, some data such 
as chlorophyll orTSS should be transformed with the log-transform to normalize the 
data. The data are transformed as they are read into the interpolator and the results 
are back-transformed to the original units in the output file. If the parameter is to be 
transformed by the natural log transform, any data values that are negative or zero 
will be set to a value of 0.0001. If the parameter is to be transformed by the square 
root transform, any data values that are negative will be set to 0.0. 

Two buttons at the bottom of the screen can be used to convert latitude and longitude 
coordinates to UTM coordinates, which are recommended for interpolation (Figures 
D-9, D-10). The first converts the longitude and latitude coordinates in d3d formatted 
files to UTM coordinates, and vice versa. This is handy for checking data locations on 
maps. The second converts individual longitudes and latitudes to and from UTM coor¬ 
dinates. NAD27 to NAD83 conversion is not supported in this code. Improper use of 
NAD27 or NAD83 can result in coordinate errors in the 100 to 300 meter range. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 



















D-9 


iEJiwSStWv-^v.^ £JmJ2 3S£2225u 


JuDtf 




Figure D-8. Data Import 
screen with fields popu¬ 
lated with data from the 
selected .d3d input file. 






L> 




Longitude/Latitude and UTM 
Converter for Interpolator Data Files 


Choose on rsput hie n d3d Iwmat «w>h longrtjde/laMude cocrAnate: 
and cconvert to UTM coot<inate; by pushng the 'Convert Lcog/Lat F*e 
to UIM F <« ' ot choose an input Me m d3d loimat svdh U T M eor»<inates 
and convert to longitude/laMude cootdmaies by pushing the "Convert 
UTM File to Long/Lat Fie'' button 

Incut He 

[CWohOVnyM*» 

Output He 

[C \Vot3f)\mytite d3d 


Btowse 


Biowre 


I 

j 


Convert LcngAat File to 
UTM Fie 


Convert UTM Fie to 
Long/Lat Fie 


Figure D-9. Data Import 
screen file converter for 
converting longitude and 
latitude 






Longitude/Latitude and UTM Converter 

Entei 4 Longitude end Latitude and push 't_L to UTM" button to 
compute UTM Coordinates Enter UTM Easting and UTM Northing 
and push "UTM to LL" button to compute the Longitude and 

Latitude 

NAD 

83? 

NAD 

[83? 



UTM 

Zone 

1 1 8 

Long 

76 

UTM 

Easting 

(412201 582184576 

Let 

38 

UTM 

Northing 

[4208286 75794108 


LL to UTM 


UTMtoLL 






Figure D-10. Data Import 
screen general purpose UTM 
converter for converting 
longitude and latitude 
coordinates to UTM 
coordinates and vice versa. 
North American Datum 
(NAD) is assumed as NAD83 
and does not convert from 
NAD27. 


appendix d 


User Guide anc! Documentation for the Chesapeake Bay Interpolator 












































D-10 


INTERPOLATE BUTTON 


Click the Interpolate Button to select the Interpolate screen (Figure D-l 1). Select 
the interpolator settings that match your requirements. The 3D Inverse-Distance 
Squared model is the 3-dimensional interpolator model. The 2D Inverse-Distance 
Squared model uses the same code as the 3D interpolator model except that only one 
layer of cells are computed—cells for each depth below the surface cell are set to 
missing (normally -9). The 2D Octant Search model computes values for cells in 
only one layer, however, the data used for computing each cell value are selected 
from data in each surrounding octant. For instance, for a given cell, the data used for 
calculation would include 4 data points from each surrounding octant, or a total of 
32 data points. The model will use fewer than the total data in each octant if insuffi¬ 
cient data exist. The model uses as many data as are available for each octant, up to 
the maximum requested number of data points. The octant search model is used to 
reduce the bias from sampling schemes that collect continuous strings of data, such 
as aircraft monitoring that collect many data points in well defined flight tracks. The 
run-time for the octant search model is significantly longer due to the extensive 
sorting required to select data from each data octant. 


The “Trace Level” selects the amount of detail written to the “.LOG” file. A “Trace 
Level” of “2” provides general interpolator statistics. A “Trace Level” of “3” 
provides information about the data values used in the computations for each region. 
A “Trace Level” of “4” provides information about individual cell computations. A 
“Trace Level” of “5”, “6”, or “7” provides increasing information about data values, 
distances, and octants. Increasing the “Trace Level” value is useful for investigating 
the performance of the interpolator. 


The “Convert .EST to .TXT” button will create a .txt file that can be imported into 
Arc/Info or Arc View. The .txt files are a full matrix of values, 57 columns wide, with 
all missing or non-existent cell values designated as missing values (normally -9), 
comma delimited, and column headings and text strings are enclosed in quotes. Each 
row in the .txt file represents numbers from 1 column of water from top to bottom, 
1 cell wide by n cells deep. Additional columns are appended to the .txt file for 
bottom, minimum, maximum, mean, and sum values. 


The “Convert .EST to .T3D” button will create a .txt file that can be imported other 
applications. The .t3d files are 4 columns wide, comma delimited, contain the x 
value, y value, negative z value, and the estimated value. There are no column head¬ 
ings. Missing values are included and are coded based on what was selected during 
the interpolation. 

The interpolator mode can be set to “Interactive” or “Batch”. In interactive mode, the 
chosen file is interpolated as defined in Figure D-l 1. In “Batch” mode, a job file is 
selected which provides the information needed to interpolate a series of files under 
machine control (Figure D-l2). The “.job” file can be built interactively by pressing 
the “Save to Batch Job” button after selecting the run parameters for each desired file 
(Figure D-l3). The “Batch Job” can be executed by pushing the “Run Batch Job” 
button. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


D-11 




C '■VolX'\BD09?0?01 cst 


,C WoOOVBDOSTCTOT log 


Convert ESI to TXT 


CHESAPEAKE BAY PROGRAM INTERPOLATOR 


P*fame<ci 1 0*U Import I / /n-»rxx>y 1 M«ilh 


Geography 


MM 


Report* 


fcP C WoTX)'vSDO9707O1 D30 


Figure D-11. 3D Inverse-Distance Squared Interpolate screen populated 
with entries after having made choices on previous screens. The 2D 
Inverse-Distance Squared Interpolate screen uses the same format as 
the 3D Inverse-Distance Squared model; however, only the surface 
depth value has computed values. Cell values at depths below surface 
are set to missing (generally -9). The 2D Octant Search Interpolator 
does not rely only on the closest data in all directions, but rather uses 
data from data from surrounding octants. For example, if 4 nearest 
neighbors are requested in each of 8 octants surrounding the cell being 
computed—up to 32 nearest neighbor values will be used to compute 
the value. If no nearest neighbor values are available, a missing value 
will be computed. Other buttons are available for creating data using 
specific formats for various GIS (.txt files) and graphics applications 
(,t3d files). 



Figure D-12. Batch Job File Name selection window that displays after 
choosing "Batch" radio button on Interpolation screen. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 
































D-12 


n 

• - . . . - ~ 



CHESAPEAKE BAY PROGRAM INTERPOLATOR 



1 

1 Data I input t I 

I //mrxwv I 

Math I Gtaptuci i ftepotu 





: WoOCHBOOWOl D3D 


■ bayjt*. Wh 


ifHiiim iBfirnwnrTiranMiMii 

|WoOCABC>G970701 •:! 




\Vol3C.\6B097C701 »g 



[t\V<*X'\b<fci9707 9709 pb 

Sav* to Batch Job § BunBatchJob 


Teji Batch Job 

■ Convert EST lo T3C 


Convert EST lo TXT 


Ewt 


Figure D-13. Saving or running a batch job through the Interpolate 
screen. "Save to Batch Job" saves the values that have been entered in 
the fields on this screen into the "Batch File ('filename'.job)". If the 'file- 
name'.job file already exists, the new entry is appended to the existing 
file. If it does not exist, a new file is created. The "Output File" file name 
entry is also written to a file ('filename'.fls) for use in creating volume 
and mass estimates by running batch jobs. The .fls file is simply a list of 
interpolated (.est) file names that can be processed sequentially. The 
"Test Batch Job" button executes a batch job but does not run the inter¬ 
polator. This button can be used to test whether the needed files exist 
and the batch job is sound prior to execution of the interpolator. 


MATH BUTTON 

Click the Math Button if you need to conduct special operations on one or more 
files. Four functions are provided: 1) Math operations which include adding, 
subtracting, multiplying, or dividing one interpolated file by another, or by a 
constant; 2) Recoding values to new values; 3) Conducting a change analysis over 
time; and, 4) Calculating the minimum or maximum values from a set of files. 

Math functionality is provided so that special parameters can be calculated. Math is 
conducted on a cell by cell basis. For instance, to add two interpolated files, Cell 1 
of input file A is added to Cell 1 of input file B and the sum is stored in Cell 1 of 
output file C, and so forth. Subtracting one file from another can be used to show 
change from one time to another (Figure D-14). Missing values are handled as in 
regular math—a non-missing value becomes missing if a math operation attempts to 
compare a real value with a missing value. Division by zero or other illegal math 
operation will cause the operation to stop. 

The “Derive New Parameter” math operations can be performed sequentially to 
provide additional capability. For instance, five interpolated files (.est files) could be 
sequentially added together, then the resulting file could be divided by 5 to compute 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 
























D-13 



Figure D-14. Math screen with files chosen to "Derive New Parameter" 
of Dissolved Oxygen by subtracting File 2 from File 1 to create the 
output file. 


the mean for the five files. Another example would be to subtract interpolated 
dissolved oxygen data from an interpolated saturated dissolved oxygen file to 
compute the oxygen deficit. 

The code checks whether the input files have the same number of segments. If the 
input files (“Input .est File 1” and “Input .est File 2”) do not have the same number 
of segments, they were generated from different bathymetry files, and the cell values 
in the two files can not be properly combined. An error message will be displayed if 
this condition occurs. 

The “Recode Parameter Value” radio button provides the means to convert calculated 
values to new values (Figure D-15). The input file is not changed, but a new output 
file is created with new values in each cell which classify the data into new values 
or categories. For example, to compute the interaction of dissolved oxygen and water 
temperature: 

1) Recode the dissolved oxygen .est file so that oxygen below 3 mg/1 is set to “1” 
and oxygen above 3 is set to “0” (also set missing to -9). 

2) Recode the water temperature .est file so that temperature below 25C is set to “0” 
and temperature above 25C is set to “10” (also set missing to -9). 

3) Derive a new parameter “WD” by adding the recoded dissolved oxygen and water 
temperature .est files. The result is a wd.est file where: “0”=acceptable oxygen and 
temperature; “l”=unacceptable oxygen; “10”=unacceptable temperature; and “11”= 
unacceptable oxygen and unacceptable temperature (missing cells will = -9). This 
file can be graphed to show the distribution of these categories. The water column 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 












D-14 



[OD«;fo^^dO«v9en.DO 1 


Recode Pai«rie<e« 


b '.-.-aap 1 

CHESAPEAKE BAY PROGRAM INTERPOLATOR 

GeoyiAfjhy I P«Mdei D*la ImfxwtlInleipolate I Hath I Giophici | RopolU 


lioul fit 1 | e-41 ■ C WdJO'JxfcvWiTOI «;l 


UJp./Helen) icWoQC, .Recode eu 


Figure D-1 5. Math screen with files chosen to "Recode Parameter 
Value" of Dissolved Oxygen by recoding values from 0-3 to the new 
value of "1", recoding values of above 3 to the new value of "0" and 
retaining missing values as -9, to create the new output file. Choosing 
a Range File is provided to load an existing set of ranges, which can 
then be modified on this screen for specialized analyses. 


volume of these categories can also be computed to show critical ranges for habitat 
analysis. 

Missing values are handled in a special way. Since missing values have no real value, 
they are not used in math operations. If a cell in either “Input File 1’’ or “Input File 
2” are flagged as missing (normally -9), then no math is done, and the “Output File” 
value for that cell is set to missing (The “Missing Value” is set on the Interpolate 
screen). 

TRENDS ANALYSIS 

Interpolated values can be analyzed for trends. The “Change Over Time” button 
allows the user to create a 3-dimensional (.est) file with linear percentage changes 
over time for each cell in the bathymetry (Figures D-16 and D-17). 

As a simple example, a station may be sampled several times over a period of time. 
The measured values can be plotted with time on the x-axis and value on the y-axis. 
The resulting linear regression line can be plotted through these points and the slope 
and intercept can be used to compute the percentage increase or decrease between 
the beginning and end ot the time series. 

This same technique can be used with the interpolator. Each cell value from a series 
of .est files can be used to compute a linear regression, so that each cell has its own 
regression and resulting percentage change, either up or down, over time. By coding 


appendix d 


User Guido and Documentation for the Chesapeake Bay Interpolator 

















D-15 



Data Impoit 


Report* 


Interpolate 


0 DisttJved Oxygen DO 1 


Compute Pei cartage Q»ange 


• ■ ‘ ■ . ■&&'£$ 


CHF.SAPf.AKF BAY PROGRAM INTERPOLATOR 

hyi/- . . - - - 


Ge<xjidphr 


lr T"' 1 Fitf-. I fhj »C \Vcl30V>do 9707 9709 >ls 


nf«< Dflrts I Ml ICWolXVCO M 


■ Oi/pU ffc I eH) 1 CWolMKir<|»e!' 


■ . -v m h m* mg 

is- . ■ 'rKt.-.i •*. i&i 


Figure D-16. Math screen with files 
chosen to "Change Over Time". The 
bdo-9707-9709.fls file contains file 
names of dissolved oxygen .EST files. 
The corresponding Julian dates for 
these .EST files are read from the do.jul 
file. In this example, a new .EST file - 
Change.est - is created which contains 
the linear trend for each cell over the 
time interval of the bdo-9707-9709.fls 
file. The Change.est cell values are per¬ 
centage change over time, categorized 
by the selection criteria identified in 
the pc.rng file. In this example, "Ignore 
missing values" has not been selected. 



CHESAPEAKE BAY 

Water Quality Analysis 

Total Phosphorus Perront Change Jul 2.1984 Dec 18, 1997 




PLAMVIFW 


Svs Cfuehanna 


Legend 

CD >103: 

■■ 53T-103: 

CD 03T-53: 
a 03:~5* 
a 5*~10Z 
E3 >103: 

E3 Not Calculated 


Atlantic 


Patuxent 
. Potomac 

A 

Rappahannock 


jf- * James 



Figure D-17. Plot of total phosphorus as a "Change Over Time". In this 
example, a new .EST file - tp8497.est - was created which contains the linear 
trend for each cell over the time interval (July 1984 through December 1997) 
of the tp8497.fls file. The tp8497.est cell values are percentage change over 
time, categorized by the selection criteria identified in the pc.rng file. The 
percentage change categories for total phosphorus mass (kg) are: >10% 
increase (red); 5 to 10% increase (pink); 0 to 5% increase (yellow); 0 to 5% 
decrease (light blue); 5 to 10% decrease (dark blue); and greater than 10% 
decrease (green). In this example, "Ignore missing values" was selected so 
that a trend on any available data was calculated. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 



























D-16 


the result as the percent change, a .est file can be created that has a percentage 
change value for each cell. This .est file represents a 3D file of “Change Over Time”. 
The plot of this file provides a graphical representation of the change. The categories 
used to display the changes graphically are defined in the pc.mg file. The default 
pc.mg file provides categories of: >+10%, +5 to +10%, 0 to +5%, -5 to 0%,— 10 to 
-5 %, and >-10 % change. These categories should be modified to reflect the needs 
of the analysis. 

Missing values in the analysis can be treated in two ways: 1) included, meaning they 
are propagated through the analysis; or, 2) ignored. The default is to include missing 
values. The result of including missing values is that if one value for a specific cell 
is missing anytime in the times series, then that cell is set to missing. The single 
missing value forces the whole series of values at that cell to be missing and no 
percentage change is calculated. The percentage change value is set to missing (-9 
by default). 

If missing values are set to be ignored, then each missing value in a time series for 
a given cell is ignored and the rest of the time series observations are used to 
compute the percentage change over time. The potential problem with this approach 
is that the trend may be skewed by the lack of having all of the desired data. 

At least two points are required to compute a time series change. If the number of 
observations for any cell is less than 2, the resulting value for the percentage change 
is set to missing. 

MINIMUM-MAXIMUM ANALYSIS 

The “Min-Max” button can be selected to locate the minimum or maximum values 
in a series of interpolated values. For instance, this function could be used to read ten 
interpolated files, and find for Cell 1 the minimum value and write that minimum 
value to the output file Cell 1. This process would be repeated for each cell, so the 
resulting output file would contain the minimum value for each cell in the series. The 
Maximum function could be chosen if desired to find the maximum cell values in a 
series of files. These functions are useful for determining, for example, the lowest 
salinity over a 10-year period, or the highest temperature over a year period, for each 
cell in the interpolated files. (Figures D-18 and D-19). 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


D-15 



CHESAPEAKE BAY PROGRAM INTERPOLATOR 


Getx*<»pG* 

gpni 



O.TV •. .vXt» -ir»W* 4 V 

■ Paiamelet I 

| IrKrrpolate jj | Graphics | 


Repotlx 


BnOi: ;Civ* <j O^syri DO I •» 11 


f ^ | fh| 

IC 9707 970? fl5 

■ inout I m! 

|C\,Vor»\DOiU 

Bui/pii F4c | e>l] 

ICWoUDVChangees' 


Compote Peitert*9* C>*r>ge 


SHHnaSni 


Figure D-16. Math screen with files 
chosen to "Change Over Time". The 
bdo-9707-9709.fls file contains file 
names of dissolved oxygen .EST files. 
The corresponding Julian dates for 
these .EST files are read from the do.jul 
file. In this example, a new .EST file - 
Change.est - is created which contains 
the linear trend for each cell over the 
time interval of the bdo-9707-9709.fls 
file. The Change.est cell values are per¬ 
centage change over time, categorized 
by the selection criteria identified in 
the pc.rng file. In this example, "Ignore 
missing values" has not been selected. 



CHESAPEAKE BAY 
Water Quality Analysis 

Total Phosphorus Percent Change J«1 2.1984-Dec 18, 199? 




PL AN VIEW 

Susquehanna 

Legend 

□ >-103: 

■■ -5*~-10X 
C=) 0*~-5* 

CD 03:~5X 
C3 5*~103: 

E3 >10X 

Not Calculated 


Atlantrc 


Patuxent “S 
Potomac 


, r- 


Jarnes 


^ Rappahannock 

I 


DEPTH PROFILE 




Figure D-17. Plot of total phosphorus as a "Change Over Time". In this 
example, a new .EST file - tp8497.est - was created which contains the linear 
trend for each cell over the time interval (July 1984 through December 1997) 
of the tp8497.fIs file. The tp8497.est cell values are percentage change over 
time, categorized by the selection criteria identified in the pc.rng file. The 
percentage change categories for total phosphorus mass (kg) are: >10% 
increase (red); 5 to 10% increase (pink); 0 to 5% increase (yellow); 0 to 5% 
decrease (light blue); 5 to 10% decrease (dark blue); and greater than 10% 
decrease (green). In this example, "Ignore missing values" was selected so 
that a trend on any available data was calculated. 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 
































D-16 


the result as the percent change, a .est file can be created that has a percentage 
change value for each cell. This .est file represents a 3D file of “Change Over Time”. 
The plot of this file provides a graphical representation of the change. The categories 
used to display the changes graphically are defined in the pc.mg file. The default 
pc.mg file provides categories of: >+10%, +5 to +10%, 0 to +5%, -5 to 0%,— 10 to 
-5 %, and >-10 % change. These categories should be modified to reflect the needs 
of the analysis. 

Missing values in the analysis can be treated in two ways: 1) included, meaning they 
are propagated through the analysis; or, 2) ignored. The default is to include missing 
values. The result of including missing values is that if one value for a specific cell 
is missing anytime in the times series, then that cell is set to missing. The single 
missing value forces the whole series of values at that cell to be missing and no 
percentage change is calculated. The percentage change value is set to missing (-9 
by default). 

If missing values are set to be ignored, then each missing value in a time series for 
a given cell is ignored and the rest of the time series observations are used to 
compute the percentage change over time. The potential problem with this approach 
is that the trend may be skewed by the lack of having all of the desired data. 

At least two points are required to compute a time series change. If the number of 
observations for any cell is less than 2, the resulting value for the percentage change 
is set to missing. 

MINIMUM-MAXIMUM ANALYSIS 

The “Min-Max” button can be selected to locate the minimum or maximum values 
in a series of interpolated values. For instance, this function could be used to read ten 
interpolated files, and find for Cell 1 the minimum value and write that minimum 
value to the output file Cell 1. This process would be repeated for each cell, so the 
resulting output file would contain the minimum value for each cell in the series. The 
Maximum function could be chosen if desired to find the maximum cell values in a 
series of files. These functions are useful for determining, for example, the lowest 
salinity over a 10-year period, or the highest temperature over a year period, for each 
cell in the interpolated files. (Figures D-18 and D-19). 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


D-17 



Repotli 


0 *(« Import 


Inietpolaie 


:0 Onrolved 00.1 


tncHi Fite; I fl;l jf \Vol3DNbrk^9707-9709 Its 


[C Wol3D\T«'Tcf *e ei» 


C Wol3DNDO WtrBrazil ed 




CHESAPEAKE BAY PROGRAM INTERPOLATOR 




fehS : - C^JvL 1 ^aJbyJsS 




Figure D-18. Min-Max screen to 
capture the minimum oxygen 
values in each cell over the July- 
September timeframe in 1997. The 
bdo-9707-9709.fls file contains file 
names of interpolated dissolved 
oxygen .EST files. The Temp file is 
an intermediate working file that 
can be deleted after the job is com¬ 
pleted. In this example, a new .EST 
file - do-minimum.est - is created 
which contains the minimum vale 
for each cell over the time interval 
of the bdo-9707-9709.fls file. In 
this example, "Ignore missing 
values" has been selected. 



Geography 


Data Import 


interpolate 


Report! 


Cote* DetnPon 


•oK-ed Owgen *ui 7 1 99 7 -$ep ’5 193' 




( HFSAPFAKF BAY PROGRAM INTERPOLATOR 


Math 


»r**'pSaie'jF*<» ' WoliiVfM't-MirvBurr ?: 


C WcOt»\DO l- 


b >^>iarv p 4e 


C WeLCSctovd t *>1 


liraphrc' *■* 


C ‘.VoODVl’O-M*™*** *n bn< 


H£ jAf 1A.» £ BA 


rY< ot Mirmurn Corvfcicri 


- Mf 




Figure D-19. Math screen with files 
chosen to "Change Over Time". The 
do97.fls file contains file names of 
dissolved oxygen .EST files. The 
corresponding Julian dates for 
these .EST files are read from the 
do.97.jul file. In this example, a 
new .EST file - dope.est - is created 
which contains the linear trend for 
each cell over the time interval of 
the do97.fls file. The dope.est cell 
values are percentage change over 
time, categorized by the selection 
criteria identified 


appendix d 


User Guido and Documentation for the Chesapeake Bay Interpolator 










































D-18 


GRAPHICS BUTTON 

Click the Graphics Button to select the Graphics screen. In this version, the graph¬ 
ical representation of the data is limited to a Plan view (looking down on the Bay and 
tidal tributary rivers) and a Side view (looking at the vertical dimension of the Bay 
and tidal tributaries from the West). 

The Graphics screen provides a means to choose all of the variables need to create 
the Bay/Trib graphic (Figure D-20). Most of the choices are driven by the files being 
graphed, to help minimize typing in all of the required information. The graphic can 
be printed or saved to a .BMP file (Figure D-21). 


Figure D-20. Graphics screen 
with default values for graphing 
the selected ".est" file. 


INfERPtUAIOn GRAPHICS 


HE1E3 



Bafrynefty f ic 




CHESAPEAKE BAY PROGRAM INTERPOLATOR 


0 «t 4 iMfXVt 


Interpolate 


Reports 


Ll VVOl JU J8\BC'03707ul ed 


t*jy 


Logorae 1 ■ br.fr 

Logo Fie 2 H 
LogoFfc 3 B \*rj 


\L»U 


B<xr»d»y Fie B vho«c_18 Ud _ 

Gioptwzi Ffc HO 


C'efe <J Mor#c*r«} 
Legwid 


L SAPt t BA -, 


atjfne'er .orrcolod by Irserpotaiior 




Dun 


Figure D-21. Graphics screen with 
titles imported from the selected 
".est" file. Click the "Interpolated 
File" button to load these titles to 
this screen. The titles can be edited 
directly on this screen. 


IMIIIIP0LA10R 



-1=1 x i 



frrfcrpotted Me 


Buxwiary Fda 


f |/xy 4 


C 3tr-/.»y 






E —l -— 


CHESAPEAKE BAY PROGRAM IN I ERPOLATOR 

9 p f Data Import r IntMpoUla j Math Q /7/jfryfcv f 


Haporlt 


(••oQiApEiy 


»wuLXVjr..’ .CuU:JaJ/Oi e* 


MS) t-*t 


lego Me*’ [J_ 

Logo Fie 3 I ^.jobtc 


£ : 0 rr»g 


Vifva* 1: • F*--i 


D W0i3C'^.BC-0S'C701 L*v 


ji Fk 


r ' • A-1 0 rvO*r, • .Hjf 7 J Iff 7 vlii ' c » 11ff • 


f nf VJT 'if Ray 


■ Wjfci uuaay 






Llfpa 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 







































































D-19 


The Batch checkbox can be selected to process a group of files (.est file names are 
read from a *.fls file) using the choices selected as shown in Figure D-22. If Batch 
is chosen, the program prompts the user to choose a *.fls file. Title 3 and Graphics 
File will automatically change for each plot based on the information contained in 
the .est file. Other titles and legends will display based on what is displayed when 
the DRAW button is pushed. Each graphic will automatically be saved to the default 
graphics file name (D-23). 


* INIIHPOLA 1 OH GRAPHICS 





[f HE SAFE At f Bay 


•V 9*<»i Qii^Wv An*tai. 


£_■ ! U,Mul 


v.-..' -J - - .. -.avL- J:-:- 

CHESAPEAKE BAY PROGRAM INTERPOLATOR 








J GeogiapHv j 

! PafMtefci 

B Data import f Inlet points j> 

Math 


Report* 


gj Inly poly >d fir H O VVOLX.'3S\eDu370?(.'l *-.i 

BMhymeby Fie BK)y rtfcMh 


L 07 »F *?1 

Logo fife 2 
logo Fib 3 


Vgtjpgjgw bnjp 


Wo bmp 


X'O 'nq 


I \ihc*e \ 8 br*d 


ID WOl 3D 38S8O097Q7O1 Urt> 


r 


Figure D-22. Graphics screen 
with legend imported from the 
selected ".rng" file. Click the 
"Categories File" button to load 
these range categories to this 
screen. These category values and 
colors can be edited directly on 
the screen. 



HI-113 


Chetapeake Bay Maimlrm 


FW BMP to Tie [ 
Pint BMP to Pirtw | 


Pt AHVITW 


CHESAPEAKE BAY 
Water Quality Analysis 

Dissolved Oxrjm Jul ?, I99" J«I 15.199' 


SviCfiJ**jr>r>n 


ntPTHPPontr 


Legend 
ED 0 0d)2 
ED o 2 l 0 
CD 1 0 3 0 
CD 3 0-5 0 
■■>5 1 ) MG/L 
1^ Not Calculated 


Atfjntv 


Figure D-23. Example graphic 
of interpolated Chesapeake Bay 
mainstem dissolved oxygen. The 
data are displayed so that the 
worst case data (low dissolved 
oxygen, in this case), regardless 
of depth, are visible in both the 
Plan- and Side views. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 











































D-20 


REPORTS BUTTON 

Click the Reports Button to generate files for volumetric and mass analysis (Figure 
D-24). 


The "Layer Thickness” is set to 0-50 meters deep to include all cells in the interpo¬ 
lated file. This thickness could be set, for example, to 3-6 meters to calculate the 
volume and mass for the water 3 to 6 meters deep (Figure D-25). 


Figure D-24. The Reports screen is 
used to compute volume and mass. 
In Interactive mode, an interpolated 
file (.est) is processed to create a 
file of water volumes by parameter 
range category by segment. A 
file of parameter mass is also 
computed by segment. If the 
"Compute Mass by Concentration 
Range" is checked on, then the 
mass calculations are also 
separated by the same category 
ranges as the volume calculations. 



C WolX\Wo9?07ti'. eti 


Vert/[>at» Ftepct' 


A/eVVotime Report 






CHESAPEAKE BAY PROGRAM INTERPOLATOR 




1 Geogtaphf j 


| Data liapoil | 

| Interpolate j 

LiJ 

Graphics 1 

I /ilKVW 


Rjr«pc Pie (r 
Output Fte i i 


Oupul Fie I r 


II Wo(X)V4orng 


|C \ValX > Vb-io97070‘ ; 


[C WoOO\t<to9?Q70l m:; 




Votr/reXtajs ( 


Figure D-25. The Reports screen 
with "Bottom Layer Thickness" 
selected and set to the bottom 
3 meters of water column depth. 

Only the cells in the selected 
bottom layer will be processed for 
volume and mass calculations. 



OUCJ fte; ma:l 






n in 'll r '' ir— It tJiL 






CHESAPEAKE BAY PROGRAM INTERPOLATOR 


Gcogt^phy 


mammict c impofl r Interpolate 


Moih 


Ui«nci 




Fla 


VOL3C>jS\8C'OS707O1 t'A 




V£>0 »r>; 


C WoOD 


Create Report Date 




appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 




















































D-21 


The resulting volume (.vol) and mass (.mas) files can be used for creating numerical 
or graphical reports, such as trends plots (Figure D-26). 

Each successive set of computed numbers are appended to the same specified 
“Output File” (.vol and .mas). 

Each successive set of computed numbers are appended to the same specified 
“Output File” (.vol and .mas) (Figure D-27). 




Figure D-26. The Reports screen 
with "Batch" mode selected. In 
Batch mode, a list of interpolated 
file names (.est) are processed 
sequentially to create a file of 
water volumes by parameter 
range category by segment. A file 
of parameter mass is also com¬ 
puted by segment. If the 
"Compute Mass by Concentration 
Range" is checked on, then the 
mass calculations are also sepa¬ 
rated by the same category 
ranges as the volume calculations. 
This example calculates volume 
and mass for the top 



TBSm H 


Data Import 


Erie! v*J) 


TuolW'M.Ua 


w t.i 


J ' C.};l %tobaa8f5&'A 
l umm m mMmrmr mm mmmasm mima mmKM m ia mam 

Tf* «c \V.;<3D\Mv_Seg'. U» IE 


V«nly 


VoklA^ct Repo** 


CHESAPEAKE BAY PROGRAM INTERPOLATOR 


— -OHM 

Geography | Pammrtni 


IrWnpolotc 


Math 


Graphics 




C WcnD\bdcS7Q7in <1 


C v .VoiX* vio trvp 


|C Wc*XAtx)o 9 70701 vol 


C \VoOD\bdo97G701 mv> 


Figure D-27. The Reports screen 
is used to compute volume and 
mass. In Interactive mode, an 
interpolated file (.est) is processed 
to create a file of water volumes 
by parameter range category by 
segment. A file of parameter mass 
is also computed by segment. If 
the "Limit Report to Selected 
Segments" is checked on, then 
the volume and mass calculations 
are computed only for the seg¬ 
ments identified in the "file¬ 
name".1st file. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


















































D-22 


Figure D-28 illustrates a time series plot of the mass of total phosphorus computed 
by the procedure described in. The mass of total phosphorus was computed for 
Chesapeake Bay and tidal tributary rivers using monthly mean data for each station 
at each depth for the period of record. The “Date” and “Total” (sum of all columns 
in mass tile) columns were used from the mass file (.mas) to make this plot. The 
linear trend line is superimposed to show the general rate of decline. This plot was 
created by opening the tp8497.mas file in Excel, selecting the line chart button, 
selecting the “Start_Date” and “Total” columns, and adjusting the legends and titles 
as necessary to create the time series plot. The linear trend was added by selecting 
the time series followed by “Chart:Add Linear Trendline”. 


Mass of Total Phosphorus in Chesapeake Bay and 
Tidal Tributaries Computed by Interpolation 


o> 

JC 

i/i 

3 

k_ 

o 

JZ 

CL 

I/I 

o 


R> 

o 


in 

n 

2 



Date 


Figure D-28. TTime series plot of the mass of total phosphorus. 


FILE DEFINITIONS AND STRUCTURE 

INPUT DATA FILE (.d3d) 

Monitoring data are required for the Interpolator to compute values. The file should 
contain one value per depth per station for which data exist. If replicate values were 
measured at some or all stations, they should be averaged at each station depth so 
that only one value exists per depth per station. The overall data can represent one 
cruise, a season of cruises, or a decade of data—there are no limitations on what the 
data represent—that is up to the user to determine. It is best, statistically, to provide 
as many data as possible. One method is to linearly interpolate values from surface 
to bottom before creating the data file for the Interpolator. This will provide more 
data for the Interpolator if it is valid to do so for the desired data. For the 2D inter¬ 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 





















polation models, only one value per station should be used, since the depth value is 
ignored. The file naming convention for the input file is ‘filename’.d3d. The input 
file has the following structure: 


D-23 


Line 1> contains a title that is meaningful to the user that identifies the contents of this dataset. 

Line 2> contains a 2-digit parameter code, comma, and the spelled-out parameter name 
Line 3> contains the start date, comma, and end date of the data 
Line 4> contains the date and time the data were compiled 
Line 5> contains the number of observations that follow 

Lines 6+> contain the easting in UTM Zone 18 meters NAD83, comma, the northing in UTM Zone 18 
meters NAD83, comma, the sample depth in meters, comma, the measured value of the parameter, 
comma, and the station ID 


CHESAPEAKE BAY AND TRIBS - Dissolved Oxygen - Measured Data -06JUL199315JUL1993 
DO,Dissolved Oxygen 
07/06/1993,07/15/1993 
08/11/1997:15:11 
1128 

407056.4377577, 0.5, 7.7000.CB 1.1 

407056.4377577, 1.0, 6.8000.CB1.I 
407056,4377577,2.0. 6.2000.CB1.1 
407056,4377577,3.0, 5.7000.CB1.1 


407056.4377577. 4.0. 

407056.4377577, 5.0, 

411793.4365898, 0.5, 

411793.4365898. 1.0, 

411793,4365898. 2.0, 

411793,4365898. 3.0, 


5.5000.CB 1.1 
5.2000.CB 1.1 
5.9000,CB2.1 
5.7000.CB2.1 
5.7000.CB2.1 
5.7000.CB2.1 


366939.4301041.0.5. 7.5000,WT8.3 
366939,4301041, 1.0, 7.3000,WT8.3 


METADATA FILE (.met) 

A metadata (documentation) file is created during the job. The default filename is 
‘filename’.met. 

Check this file (using the Notepad editor) to see what calculations were performed 
during the job. 


Statistics Report for C:\Vol3D\BD0930701 .est 

Title: CHESAPEAKE BAY AND TRIBS Dissolved Oxygen Measured Data 

06JUL199315JUL1993 

Parameter: Dissolved Oxygen Parameter Code: DO 
Data Period: 07/06/199307/15/1993 
Data File Date: 08/11/1997:15:11 
Observations: 1128 

Maximum Number of Nearest Neighbors: 4 
Minimum Number of Nearest Neighbors: 1 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 









D-24 


Maximum Vertical Search Window: 4 
Minimum Vertical Search Window: 0 
Vertical Search Window Step Size: .5 
Maximum Horizontal Search Radius: 25000 
Missing Value: 9 

Interpolator Model: DepthRadiusInterpolator 
Interpolation Date: 10/6/97 10:55:55 AM 
Bathymetry File: bay_trib.bth 
Number of Bathymetry Regions: 68 
Data Region File: bay_trib.reg 
Number of Data Regions: 68 

- Bathymetry Region ID: 1001 Region Name: CB1TF Data Points: 28 in data region 1001 
-Cell Size EW: 1000 NS: 1000 Vertical: 1 

— 360 cells were interpolated in region CB1TF Subtotal: 360 total cells 

— Region was calculated in 4 seconds. 

- Bathymetry Region ID: 1002 Region Name: CB20H Data Points: 48 in data region 1002 

- Cell Size EW: 1000 NS: 1000 Vertical: 1 

— 1237 cells were interpolated in region CB20H Subtotal: 1597 total cells 
-Region was calculated in 1 seconds. 


Total Number of Cells Interpolated: 173805 

Total Number of NonMissing Value Cells Interpolated: 160558 

Total Number of Missing Value Cells Interpolated: 13247 


Nearest Neighbors: 4 
Nearest Neighbors: 3 
Nearest Neighbors: 2 
Nearest Neighbors: 1 


# of Cells: 101978 

# of Cells: 12358 

# of Cells: 33517 

# of Cells: 12705 


173805 cells were calculated in 244 seconds. 


INTERPOLATED ESTIMATES FILE (.est) 

An interpolated estimates file is created during the job. The default filename is ‘file¬ 
name’.est. For the 3D interpolator model, this file contains the values for each cell 
interpolated during the job, from surface to bottom for each cell location. For the 2D 
interpolator models, this file contains the values for the top cell at each cell location. 
While the interpolated value is written to the surface cell location in this file, its 
value might represent the bottom value—i.e., the value might represent bottom layer 
dissolved oxygen. All cell values below the top value will be set to missing (usually 
-9). The file contents include: 

Line l>Input data file name 
Line 2>Data file description 

Line 3> 2 digit parameter code and parameter name 

Line 4>Start and end dates of data 

Line 5>Date and time data file was compiled 

Line 6>Number of data points, nearest neighbors, minimum neighbors, maximum vertical window, 
minimum vertical window, vertical window step increase size, maximum search radius, missing value 
Line 7>Name of interpolator used 
Line 8>Date and time of job 
Line 9>Bathymetry file used 


appendix d 


User Guido and Documentation for the Chesapeake Bay Interpolator 





D-25 


Line 10>Data region file used 

Line 1 l>Number of segments to interpolate 

Line 12>Cell description for this segment:number of surface cells in segment, segment id, segment 
name, cell e-w dimension in meters, cell n-s dimension in meters, cell vertical depth in meters 
Line 13+>cell easting, cell northing, cells deep, interpolated values from surface to bottom. 


C:\VOL3D\BDO970601.D3D 

CHESAPEAKE BAY BY CRUISE - Dissolved Oxygen - Linear Interpolated Data - 
3JUN199712JUN1997 
DO,Dissolved Oxygen 
06/03/1997,06/12/1997 
06/10/1998:8:55 
1254,4,1,4,0,-5,25000,9 
Interpolator Model: DepthRadiusInterpolator 
6/17/98 10:24:26 AM 
cbay8.bth 
cbay8.reg 
8 

132,100 LCB1TF, 1000,1000,1 

403000.4384000.2.9.1.8.9 

404000.4384000.5.9.1.8.9.8.8.8.8.8.8 

404000.4383000.3.9.1.8.9.8.8 

405000.4383000.8.10.3.9.9.9.5.8.8.8.8.8.8.8.8.8.8 

405000,4382000,3,10.3,9.9,9.5 

406000.4382000.1.9.9 


405000.4082000.1.10.5 
410000,4082000,4,10.5,10.5,10.4,10.3 

404000.4081000.1.10.6 
410000.4081000,3.10.5.10.5,10.4 

405000.4079000.1.10.6 


TXT FILE CREATED FROM ESTIMATES FILE (.txt) 

Interpolated estimates files ( ‘filename’.est) can be reformatted as ‘filename’.txt files 
which can be readily imported into other applications, including Arc/Info and 
Arc View. The .TXT file contains the values for each cell in the original Estimates 
file, from surface to bottom for each cell location. In addition, each line in the file is 
padded with -9 values. So the file is a rectangular matrix of data with all values 
having a value. The file is comma delimited, and all extraneous blanks have been 
removed. The precision of the reported parameter values are assigned by the values 
set in the ‘parameter.sys’ file. The .TXT file contents include: 


Line l>Coiumn Headings 

Line 2+>cell easting, cell northing, segment name, cell e-w dimension in meters, cell n-s dimension in 
meters, cell vertical depth in meters, bathymetry depth in meters, interpolated values from surface to 
bottom, additional depths padded with -9 down to !ayer_45, then bottom, minimum, maximum, mean, 
and sum values for non-missing cells in this water column. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 






D-26 


UTM_X.UTM_Y.Segment > EW_dim,NS_dim.Vert_dim,n,layer_l,layer_2,layer_3,layer_4,layer_5,laye 
r_6,layer_7,layer_8,layer_9,layer_ 10,layer_ 11 ,layer_ 12,layer_ 13,layer_ 14,layer_ 15,layer_ 16,layer_ 17 
,layer_18,layer_19,layer_20,layer_21,layer_22,layer_23,layer_24.layer_25,layer_26,layer_27,layer_2 
8,layer_29,layer_30,layer_31 ,layer_32,layer_33,layer_34,layer_35,layer_36,layer_37.1ayer_38,layer_ 
39,layer_40,layer_41,layer_42,layer_43,layer_44,layer_45,Bottom.Minimum.Maxi mum,Mean.Sum 


403000.4384000, CB1TF, 1000,1000,1,2.-9.0,-9.0,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.-9,-9,-9.-9.-9.-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.0,-9.0.-9.0.-9.0.-9.0 

404000.4384000. CB 1 TF. 1000,1000,1,5,-9.0,-9.0,-9.0,-9.0,-9.0,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.-9,-9,-9,-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.0,-9.0,-9.0,-9.0,-9.0 


383000.4338000, CB3MH,1000,1000, l,6,8.7,8.2,8.0,7.4,6.2,4.7,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,4.7.4.7,8.7,7.2,43.2 

384000.4338000, CB3MH, 1000,1000,1,5,8.7,8.2,8.0,7.4,6.2,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,6.2,6.2,8.7,7.7,38.5 

385000.4338000, CB3MH,1000,1000,1,6,8.7,8.2,7.9,7.3,6.2,4.8,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,4.8,4.8,8.7,7.2,43.1 

386000.4338000, CB3MH, 1000,1000,1,6,8.7.8.1,7.9,7.3,6.2,4.9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.-9,-9,- 
9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9,-9.-9,-9,-9,-9,-9,-9,-9,-9,-9.4.9,4.9,8.7,7.2,43.1 


T3D FILE CREATED FROM ESTIMATES FILE (,t3d) 

Interpolated estimates files (‘filename’.est) can be reformatted as ‘filename’.t3d files 
which can be readily imported into other applications, such as, NoeSys and T3D. 
The ,T3D file contains the values for each cell in the original Estimates file, with one 
cell value per line in the output file. The file is comma delimited, and all extraneous 
blanks have been removed. The precision of the reported parameter values are 
assigned by the values set in the ‘parameter.sys’ file. The ,T3D file contents include: 

Line l+>cell centroid easting, cell centroid northing, negative cell centroid depth in meters, interpo¬ 
lated value for parameter 


403000.4384000, -0.5,-9.0 

403000.4384000, -1.5.-9.0 

404000.4384000, -0.5,-9.0 

404000.4384000, -1.5,-9.0 


391500.4304000. -9.5,0.8 

391500.4304000. -10.5.0.2 

391500.4304000, -11.5.0.1 

391500.4304000, -12.5,0.1 


BATHYMETRY FILE (,bth) 

Each interpolator job requires a bathymetry file which defines the cell structure of 
the desired body of water that is being interpolated. The following shows the 
contents of the cbayS.bth file: 

Line l>Number of segments to interpolate 

Line 2>Number of surface cells in segment 1, segment id, segment name, e-w cell size in meters, n-s 
cell size in meters, cell depth in meters 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 






D-27 


Line 3>Cell centroid easting in meters, cell centroid northing in meters, number of cells (>0) from 
surface to bottom, cell centroid depths from surface to bottom. The Interpolator computes a value for 
each cell centroid identified in the .bth file which is output to the .est file. 

(Repeat 2 & 3 for each segment.) 


8 

132.1001. CB1TF, 1000,1000.1 
403000,4384000.2,0.5,1.5 
404000.4384000,5,0.5.1,5.2.5.3.5,4.5 
404000,4383000.3,0.5,1.5,2.5 
405000,4383000,8.0.5,1.5,2.5,3.5,4.5.5.5.6.5,7.5 
405000.4382000.3,0.5,1.5,2.5 

410000.4363000,6.0.5,1,5.2.5.3.5.4.5,5.5 
411000,4363000.10,0.5,1.5.2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5 
412000,4363000,4.0.5.1,5,2.5,3.5 

270.1002. CB20H, 1000,1000,1 
403000.4363000.3,0.5.1.5.2.5 
404000,4363000.4,0.5.1,5,2.5,3.5 
405000.4363000,3,0.5.1.5,2.5 
406000,4363000.3,0.5.1.5,2.5 
400000.4362000,1,0.5 

385000.4107000,1.0.5 

381000,4106000,2,0.5,1.5 

381000,4105000,2,0.5,1.5 

381000.4104000,1.0.5 


DATA REGIONS FILES (regions.sys, .reg) 

Each interpolator job requires a data regions file which defines the geographic 
boundary of the data for the body of water that is being interpolated. 77 data regions 
have been created, one for each CBP segment. The data region is used to clip off data 
that fall outside the desired geographic area that is being interpolated. A data regions 
file includes one or more data region definitions that must match the bathymetry 
being interpolated. These data region file names are stored in a file, regions.sys, 
which is required by the Interpolator. This file can contain 25 defined regions files. 
The order of the entries in this file define the order presented to the user in the 
GEOGRAPHY screen during the job. The structure of the regions.sys file is: 

Line l+>Item identifier (sequential number of 1 to 25). comma, data region name, comma, 
corresponding bathymetry file name, comma, corresponding data region file name. 

Repeat for each defined data region. 


1. Bay and Tribs.bay_trib.bth,bay_trib.reg 

2, Chesapeake Mainstem (CB ICB8),cbay8.bth,cbay8.reg 


Each .reg file defined in the regions.sys file must have the following structure. The following shows the 
contents of the cbay8.reg file: 

Line l>Bathymetry file name 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 











D-28 


Line 2>Data region file name 
Line 3>Number of segments to interpolate 
Line 4>First segment ID and name 
Line 5> Data region ID 

Line 6>Number of x-y points in this data region 

Line 7+>Data region x-y points. First and last in each polygon must be the same to close the polygon. 
Repeat for each data region. 


cbay8.bth 

cbay8.reg 

8 

100LCB1TF 

1001 

8 

398699.4385013 
421073.4384159 
413328,4355973 
397839.4344869 
383210.4352557 
401281.4367077 

398699.4385013 

398699,4385013 


1008.CB8PH 

1008 

13 

410677,4131611 

418631,4108780 

422019,4095375 

415538,4079614 

408467,4086243 

396978,4083150 

384016.4087569 

372968.4087569 
372968,4095817 
385194,4104508 
374588,4117618 
386372,4131464 
410677,4131611 


PARAMETER NAMES FILE (params.sys) 

Each parameter is identified by a 2-digit parameter code and spelled out parameter 
name. These codes and names are stored in the params.sys file. This file can accom¬ 
modate 25 parameters. The order of the codes and names in this file determines the 
order of the parameters in the PARAMETERS screen (Figure D-3). This file can be 
edited as necessary by the user. The file structure is: 

Line l>Item number (up to 25 lines), comma, spelled out parameter name, comma, 2-digit parameter 
code, comma, number of digits precision to the right of the decimal in output file. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 






D-29 


1, Dissolved Oxygen.DO, 1 

2, Chlorophyll,CH.l 
3,Salinity ,SA,1 

4, Water Temperature.WT, 1 

5, Total Nitrogen.TN,2 

6, Ammonia,NH,3 

7, Nitrite.N2,3 

8, Nitrate,N3,3 

9, Total Phosphorus,TP,2 


PARAMETER RANGE FILES (.rng) 

Several files are required to compute report files of volume and mass and to graphi¬ 
cally portray the interpolated results. These include: 

1) the “filename”.est file of estimated values; 

2) the cbpotiny.bmp which is a small CBP logo file; 

3) the aro.bmp which is a small north arrow; 

4) shore_18.bnd which is a shoreline boundary file; and, 

5) the “parameter_code”.mg file. The range file is used by the volume and mass 
report procedures to subset the computed results into categories for reporting. For 
graphics, the .mg file defines how the graphics program assigns colors to each cell 
value in the “filename”.est file. For drawing purposes, the first range in the .mg table 
has drawing priority over the second range, which has priority over the third range, 
etc, so the first range color will paint over ranges lower in the table. This order deter¬ 
mines which colors have priority in the final graphic. The do.mg file serves as an 
example: 


Line l>For dissolved oxygen values of 0.0 to but less than 0.2, color 12, pattern 0, title 0.0-0.2 

Line 2>For dissolved oxygen values of 0.2 to but less than 1.0, color 13. pattern 2, title 0.2-1.0 

Line 3>For dissolved oxygen values of 1.0 to but less than 3.0, color 14, pattern 12, title 1.0-3.0 

Line 4>For dissolved oxygen values of 3.0 to but less than 5.0, color 11, pattern 12, title 3.0-5.0 

Line 5>For dissolved oxygen values of 5.0 to but less than 25.0, color 19, pattern 21, title >5.0 MG/L 
Line 6>For dissolved oxygen values of -10.0 to but less than -8.0 (-9=missing value), color 8, pattern 
8, title Not Calculated 

Pattern is currently ignored in this version. 

To categorize integer value ranges, it is best to bracket the range, for instance, to 
assign color 12 to the range of 2 (lower bound) to 2 (upper bound), set the lower 
bound to 1.9 and the upper bound to 2.1. Set the title to “2” to convey the intent that 
“2” is the range being presented. This bracketing is required to allow the code to 
select values equal to or greater than the lower bound and less than the upper bound. 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 


D-30 


Acceptable color codes are: 


0 

Black 

1 

Blue 

2 

Green 

3 

Cyan 

4 

Red 

5 

Magenta 

6 

Yellow 

7 

White 

8 

Gray 

9 

Light Blue 

10 

Light Green 

11 

Light Cyan 

12 

Light Red 

13 

Light Magenta 

14 

Light Yellow 

15 

Bright White 


0 . 0 , 0 . 2 , 12 , 0 , 0 . 00.2 
0.2,1.0,13,2,0.21.0 
1.0,3.0,14,12,1.03.0 
3.0,5.0,11,12,3.05.0 
5.0,25.0,9,21,>5.0 MG/L 
10.0,8.0,8,8,Not Calculated 


EXAMPLE BATCH JOB FILE (.job) 

Individual interpolator runs can be computed sequentially by saving the necessary 
information for the run in a “Batch File” (‘filename’.job). This “Batch File” is then 
used to calculate each of the files identified in the .job file. This file can be edited 
as necessary by the user. The file structure is: 


Line l>bathymetry file 

Line 2>regions file 

Line 3>input data file 

Line 4> output interpolated (.est) file 

Line 5> output metadata (.met) file 

Line 6>parameter transformation 

Line 7>minimum number of neighbors 

Line 8>maximum number of neighbors 

Line 9>horizontal range (m) 

Line 10>vertical range minimum 

Line 1 l>vertical range maximum 

Line 12>Vertical step size 

Line 13>missing value 

Line 14>interactive/batch flag 

Repeat lines 1-14 for each file to be interpolated 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 








D-31 


cbay8.bth 

cbay8.reg 

C:\Vol3D\BD0970601.D3D 
C:\Vol3D\BD0970601.est 
C:\Vol3D\BD0970601 .met 
None 
1 
4 

25000 

0 

4 

.5 

-9 

1 

cbay8.bth 

cbay8.reg 

C:\Vol3D\BD0970701 .D3D 
C:\Vol3DMBD097070l.est 
C:\Vol3D\BD0970701 .met 
None 


4 

25000 

0 

4 

.5 

-9 

1 


EXAMPLE BATCH JOB FILE LIST (.fls) 

Calculations on individual interpolator .est files can be computed sequentially by 
reading the .est file names from a “batch file list” (‘filename’.fls). This file is created 
when the Batch Job File (‘filename’.job) is created. This file can be edited as neces¬ 
sary by the user. The file structure is: 

Line ^interpolator (.est) file name 

Repeat for each file to be processed. 


C:\Vol3DVBD0970601.est 

C:\Vol3D\BD0970701.est 


EXAMPLE JULIAN DATE FILE (.jul) 

Calculations of “Change Over Time” require a julian date file which contains the 
dates which relate to the .est files identified in the .fls file. For this analysis, the julian 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 









D-32 

♦ 

y 


dates are the X variable of the time series and the .est files are the Y variables of the 
time series. The julian dates represent dates and times that are based on the decimal 
numbering system, rather than years, months, and days (and time). The julian (or any 
linearly numbered scheme) date file must be created by the user. The following 
example was created by opening the appropriate .mas file in Excel, converting the 
“Start_Date” from mm/dd/yy format to decimal format, and cutting and pasting the 
reformatted date column into a flat file. The file structure is: 

Line l>julian date 

Repeat for each file to be processed. 


30865.00 

30895.00 

30929.00 

30956.00 


EXAMPLE SEGMENTS LIST FILE (.1st) 

Reports on individual segments can be computed sequentially by reading the .est file 
names from a “segments list file” (‘filename’.1st). This file is created manually by 
the user. The file structure is: 


Line l>Number of segments to process 

Line 2+>Segment name (spelling must match those in Appendix A). 
Repeat for each segment to be processed. 


3 

POCTF 

POCOH 

POCMH 


EXAMPLE VOLUME REPORT FILE (.vol) 

The volume of water that contains a specified range of concentrations of a parameter 
can be computed and saved to a volume report file (‘filename’.vol). Volume esti- 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 













D-33 


mates are reported in liters. The entire Bay and tributary volume based on the “Bay 
and Tributary” 77 segment bathymetry is 75,199,817,500 m A 3, or 75.2xlO A 12 liters. 
The volume of Main Bay segments CB1TF-CB8PH totals 51.839xl0 A 12 liters. Data 
from one job to another may be appended to the same output File so that a time series 
file is created that can be opened in a spreadsheet or database program for further 
graphing or analysis. The first line of the file is composed of ‘column headings’ 
contained within quotes. This file can be edited as necessary by the user. The file 
structure is: 


Line l>Column headings defined by the ’Report’ job that is run, including data start date, data end 
date, depth of top layer analyzed, depth of bottom layer analyzed, volume for segment, volume by 

concentration range for that segment,.repeat for each segment,...,grand total volume 

Line 2>data accumulated from the input interpolated file (.est) for each column in line 1 
Repeat for each interpolated file processed. 

Note: Since data that are calculated may be appended to an existing file, there is a 
risk that the user may append data from different bathymetry jobs. The user must be 
careful not to mix 8 segment mainstem data with 77 segment mainstem and tributary 
data in this report, or else the column headings will not represent the data. 


“Start Date”,"End Date”,"Layer Top”,"Layer 

Bottom",”CB1TF" I "CB1TF_0.0_0.2","CB1TF_0.2_1.0”,"CB1TF_1.0_3.0”,”CB1TF_3.0_5.0”,”CB 
1 TF_5.0_25.0”,”CB 1 T F _ - 1 0 . 0 _ - 

8.0","CB20H","CB20H_0.0_0.2","CB20H_0.2_1.0”,”CB2OH_1.0_3.0","CB20H_3.0_5.0”,"CB 
20H_5.0_25.0","CB20H_-1 0 . 0 _ - 

8.0","CB3MH”,”CB3MH_0.0_0.2",”CB3MH_0.2_1.0”,”CB3MH_1.0_3.0”,"CB3MH_3.0_5.0",”CB 
3MH_5.0_25.0"," CB3MH_-1 0.0_- 
8.0”,”CB4MH","CB4MH_0.0_0.2",”CB4MH_0.2_1.0","CB4MH_1.0_3.0”,”CB4MH_3.0_5'.0”,"CB 
4MH_5.0_25.0" ,"CB4MH_-1 0.0_- 

8.0",”CB5MH",”CB5MH_0.0_0.2”,”CB5MH_0.2_1.0",”CB5MH_1.0_3.0",”CB5MH_3.0_5.0","CB 
5MH_5.0_25.0",”CB5MH_-1 0.0_- 

8.0","CB6PH" ”CB6PH_0.0_0.2”,”CB6PH_0.2_1.0”,"CB6PH_1.0_3.0","CB6PH_3.0_5.0",”CB6 
PH_5.0_25.0","CB6PH_-1 0.0_- 

8.0" ”CB7PH","CB7PH_0.0_0.2","CB7PH_0.2_1.0",”CB7PH_1.0_3.0”,"CB7PH_3.0_5.0”,"CB7 
PH_5.0_25.0","CB7PH_-1 0 . 0 _ - 

8.0”,”CB8PH”,”CB8PH_0.0_0.2”,”CB8PH_0.2_1.0”,"CB8PH_1.0_3.0",”CB8PH_3.0_5.0”,”CB8 
PH_5.0_25.0",”CB8PH_-10.0_-8.0",’Total” 

“06/03/1997”,"06/12/1997”,0.,50., 359000000000.,0..0..0..0.,359000000000.,0., 12370000000 
00.,0.,0.,0.,56000000000., 1181000000000., 0.,2391000000000.,0.,2000000000., 366000000 
000. ,361000000000., 1662000000000. ,0. ,9237000000000. ,0., 19000000000., 197400000000 
0., 1104000000000.,6140000000000.,0.,15377000000000.,0.,0.,0.,851000000000.,1452600 
0000000.,0.,6503000000000.,0.,0.,0.,0.,6503000000000.,0.,13488000000000.,0.,0.,0.,0.,13 

488000000000.. 0..3150000000000..0..0..0..0..3150000000000..0..51742000000000..0..210 
00000000. ,2340000000000. ,2372000000000. ,47009000000000. ,0. 

“07/07/1997",”07/15/1997”,0.,50.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,2391000000000., 13400 
0000000.,51000000000.,1 50000000000.,168000000000., 1888000000000.,0.,92370000000 
00., 1937000000000., 1253000000000.,933000000000.,620000000000.,4494000000000. ,0., 

15395000000000., 1391000000000.,2236000000000.,1909000000000.,1069000000000.,87 

90000000000.. 0..6503000000000..0..0..524000000000., 1063000000000.,4916000000000., 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 





D-34 

/ 


0..13482000000000.,0.,0.,661000000000.,2978000000000.,9843000000000. ,0.,247400000 
0000. ,0. ,0.,0., 114000000000. ,2360000000000. ,0. ,49482000000000. ,3462000000000., 3540 
000000000.,4177000000000.,6012000000000.,32291000000000.,0. 

“07/16/1997”,"07/31/1997”,0.,50.,360000000000.,0.,0.,0.,0.,360000000000.,0.,12370000000 
00.,0.,0.,7000000000.,24000000000.,1206000000000.,0.,2391000000000.,0., 13300000000 
0.,159000000000.,282000000000.,1817000000000.,0.,9237000000000.,0., 1620000000000. 
, 1644000000000.,942000000000.,5031000000000.,0.,15388000000000.,0., 1453000000000 
.,2710000000000.,2005000000000.,9220000000000.,0.,6503000000000.,0.,0.,5500000000 
0..1231000000000.,5217000000000.,0.,13491000000000., 0 ., 0 ., 9000000000., 17930000000 
00..11689000000000.,0.,3160000000000.,0.,0.,0.,0.,3160000000000.,0.,51767000000000., 
0.,3206000000000.,4584000000000.,6277000000000.,37700000000000.,0. 


EXAMPLE MASS REPORT FILE (.mas) 

The mass file report contains the mass of a parameter computed for each cell in the 
interpolated (.est) file then summed in one of two ways. The default method (below 
example) is to sum the mass by segment and total for all segments in the bathymetry. 

The second method follows the format of the volume report and computes the mass 
bv concentration rang e for each segment. 

The mass that is computed and summed is saved to a mass report file (‘file¬ 
name’.mas). It is assumed the input data are measured in [units]/[liter], such as mg/1 
or ug/1 or counts/liter. In the mass report, the resulting mass estimates are computed 
by multiplying the [estimated concentration in the cell (often in mg/1)] * [the volume 
of the cell in m A 3 (for instance, 1000m east-west x 1000m north-south x lm deep)] 
* [1000 l/m A 3 to convert from m A 3 to liters]. Hence, if the input data were in mg/1 
and then the concentration is estimated to be 6mg/l in a cell, the resulting mass will 
be 6*10 A 9 mg for a 1km x lkm x 1 m cell. As a second example, if the input data 
were in mg/m A 3, which is equivalent to ug/1, then the reported mass values would be 
in micrograms to account for the volume being reported in m A 3 rather than liters. If 
the input data are counts (such as organism counts) per liter, then the mass report 
units would be total counts. If the input data are counts (such as organism counts) 
per cubic meter, then the total counts in the mass report must be divided by 1000 to 
account for the conversion from cubic meters to liters between the input data and the 
interpolated counts. The mass (or counts) for each cell is then summed for a total 
mass (or count) in the segment and also a grand sum of mass (or count) for the total 
for all segments under analysis. For instance, if the input data for CHLA were meas¬ 
ured as ug/1 and the resulting mass in Segment CB20H was reported after 
interpolation as 13,000,000,000,000, that represents 1.3 A 13 ug CHLA for Segment 
CB20H—i.e 1.3 A 13ug / 1.237 A 12 liters in CB2OH=10.5 ug/1 average. As a second 
example, if the input data were for mg biomass of organisms per cubic meter and the 
resulting mass in Segment CB20H was reported as 132,627,709,873,200, that repre¬ 
sents 1.326 A 14 / 1000 mg for Segment CB20H, since an adjustment for the input 
data must be made for the per cubic meter to per liter basis. A quick check can be 
made by multiplying the average input data value by the volume of a segment to 
determine if the results are within reason. For instance, if there were approximately 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 





D-35 

-if 


150 mg biomass per cubic meter in the monitoring data for CB20H, that would be 
[150 mg/m A 3] * [1,237,000,000 cubic meters in Segment CB20H] = 1.86 A 11 mg 
biomass in the Segment CB20H, which is close to the interpolated value of 
1.326 A 11 mg, above. 

Data from one job to another may be appended to the same output file so that a time 
series file is created that can be opened in a spreadsheet or database program for 
further graphing or analysis. The first line of the file is composed of ‘column head¬ 
ings’ contained within quotes. This file can be edited as necessary by the user. The 
file structure is: 


Line l>Column headings defined by the ‘Report’ job that is run, including data start date, data end 
date, depth of top layer analyzed, depth of bottom layer analyzed, mass of parameter by 
segment.repeat for each segment.grand total mass 

Line 2>data accumulated from the input interpolated file (.est) for each column in line 1 

Repeat for each interpolated file processed. 

Note: Since data that are calculated may be appended to an existing file, there is a 
risk that the user may append data from different bathymetry jobs. The user must be 
careful not to mix 8 segment mainstem data with 77 segment mainstem and tributary 
data in this report, or else the column headings will not represent the data. 


“Start Date”,“End Date”,’’Layer Top",’’Layer 

Bottom”,”CBlTF’,”CB20H”,”CB3MH”,"CB4MH",”CB5MH”,”CB6PH”,”CB7PH’’,”CB8PH”,’’Total” 
“06/03/1997”,”06/12/1997”,0.,50.,33956000.,99418000.,165733000.,602087001., 1229455000.,56352 

3000., 1121378003.,301820000.,4117370004. 

“07/07/1997”,”07/15/1997”,0.,50.,0.,0.,149745000.,378545000.,726926999.,429706000.,871456001., 

173612000.. 2729991001. 

“07/16/1997”,"07/31 /1997”,0.,50.,21746000..82626000., 150816000..438919000.,795246002.,428770 
000,917109001 .,243880000.,3079112003. 

“08/04/1997”,”08/14/1997”,0.,50.,22096000„73961000.,128601000.,465053000.,824646000.,456154 
001 .,940162001 .,236345001 .,3147018002. 

“08/18/1997”,”08/28/1997”,0.,50„ 19932000.,82306000., 15 8793000.,460818001 .,885485001 .,470413 
001.,1010405001.,231651001.,3319803005. 

“09/02/1997” ,”09/15/1997”,0. ,50., 19748000. ,76169000., 128181000. .517683000., 1009546002. ,45963 
2001 ..949621002..227962000..3388542005. 

“ 10/06/1997”,” 10/15/1997”,0.,50.,0.,0., 173127001.,576579000.,1004260004.,482547000.,963827000. 
,200433996.,3400774001. 


EXAMPLE INTERPOLATOR JOB 

1) Double click the Vol3D.exe icon to run the Interpolator program. 

2) Click the GEOGRAPHY button to display the GEOGRAPHY screen. 

3) Choose “Chesapeake Mainstem (CB1-CB8)” to interpolate the Main Bay. 

4) Click the PARAMETER button to display the PARAMETER screen. 


appendix d • User Guide and Documentation for the Chesapeake Bay Interpolator 










D-36 


'k 


5) Choose “DO- Dissolved Oxygen” as the parameter. 

6) Click the DATA IMPORT button to display the DATA IMPORT screen. 

7) Click Get File Name button and select the “C:\Vol3D\BD0970701.D3D” data 
file. The file name, start and end dates, number of observations, file date, parameter, 
code, and title should appear in the Data Import screen. If not, you selected an incor¬ 
rect file. 

8) Click the INTERPOLATE button to display the INTERPOLATE screen. The 
input file should read “C:\Vol3D\BD0970701.D3D”, the output file should read 
“C:\Vol3D\BD0970701.esf\ Bathy file should read “cbay8.bth”, and metadata file 
should read “C:\Vol3D\BD0970701.met”. 

9) Click the “Run Interpolation” button to create the standard *.est file or click the 
“Also Create TXT File” to create a “filename”.txt file that can be imported into 
Arc/Info or ArcView as a table. The text Arc View file will be approximately 2.3 mb 
in size. 

10) If you created an interpolated .est file, you can view the results by clicking the 
GRAPHICS button to display the GRAPHICS screen. If you created a .txt file to 
load into an ArcView table, you can quit the Interpolator program and continue 
working with the output file in ArcView. 

11) At the GRAPHICS screen, the Interpolated file should read 
“C:\Vol3DVBD0970701.est”. Click the Interpolated File button to load titles and 
dates for the graphic. 

12) The Bathymetry file should read “cbay8.bth”, the Logo File 1 should read 
“Acbpotiny.bmp”, the Logo File 3 should read “Aaro.bmp”, the Categories File 
should read “.\DO.mg”. Click the Categories File button to load the categories for 
the graphic. The Boundary File should read “Ashore_18.bnd”, the output Graphics 
File name should read “CAVol3D\BDb970701.bmp”. The titles and legends were 
loaded by pushing the Interpolated File and Categories File buttons. The background 
color of the boundary file is set by clicking the small grey box to the right of the 
Boundary File name. Click “Plot Points” ON if you want to display the location of 
the monitoring stations. Click “Plot Data Regions” ON if you wish to see the Data 
Region polygons. Choose “Minimum” if you wish to display the minimum color 
value (where minimum is the worst case, such as dissolved oxygen), or choose 
“Maximum” if you wish to display the maximum color value (where maximum is 
the worst case, such as temperature), or choose the “Top/West” edge or 
“Bottom/Easf ’ edge to display the desired side. 

Titles, categories, colors, and legends can be modified on this screen and will be 
reflected in the resulting drawing. Clicking the “DRAW” button will draw the image 
in a graphics window. The graphics window can be saved to a file or printed. This 
version of the Interpolator does not allow graphical editing. The saved “file¬ 
name”.bmp file can be edited in a commercial graphics editing package, such as 
Lview Pro or Corel Draw. The .bmp file can be converted to gif or jpeg format for 
publication on the web. 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


APPENDIX 


D-37 


Segment Name, EW-Dimension, NS-Dimension, Depth Dimension, Number Cells in Segment, 
Segment Volume 

CB1TF, 1000,1000,1,360,360000000 
CB20H, 1000,1000,1,1237,1237000000 
CB3MH, 1000,1000,1,2391,2391000000 
CB4MH, 1000,1000,1,9237,9237000000 
CB5 MH, 1000,1000,1,15416,15416000000 
CB6PH, 1000,1000,1,6503,6503000000 
CB7PH, 1000,1000,1,13523,13523000000 
CB8PH, 1000,1000,1,3172,3172000000 
NORTF.500,500,1,106,26500000 
C&DOH, 100,100,1,2413,24130000 
ELKOH,500,500,1,405,101250000 
BOHOH,250,250,1,272,17000000 
S ASOH,250,250,1,1347,84187500 
CHSTF.50,50,1,1345,3362500 
CHSOH,250,250,1,462,28875000 
CHSMH,500,500,1,1821,455250000 
EASMH.500,500,1,3987,996750000 
CHOTF,50,50,1,6129,15322500 
CHOOH.250,250,1,722,45125000 
CHOMH2,500,500,1,1067,266750000 
CHOMH1,1000,1000,1,945,945000000 
LCHMH.500,500,1,833,208250000 
HNGMH, 100,100,1,18568,185680000 
FSBMH, 1000,1000,1,143,143000000 
NANTF,50,50,1,2646,6615000 
NANOH,50,50,1,18000,45000000 
NANMH,500,500,1,389,97250000 
WICMH. 100.100,1,5642,56420000 
M ANMH.500,500,1,358,89500000 
BIGMH.250,250,1,698,43625000 
POCTF,50,50,1,1788,4470000 
POCOH.50,50,1,7200,18000000 
POCMH,500,500,1,1418,354500000 
TANMH, 1000,1000,1,4019,4019000000 
BSHOH,500,500,1,197,49250000 
GUNOH.500,500,1,257,64250000 
MIDOH,250,250,1,400,25000000 
B ACOH.250.250,1,358,22375000 
PATMH,500,500,1,1806,451500000 
MAGMH.250,250,1.1224,76500000 
SEVMH,250,250,1,1815,113437500 
SOUMH,250,250,1,1072,67000000 
RHDMH.250,250,1,325,20312500 
WSTMH,250,250,1,326,20375000 
PAXTF.50,50,1,4410,11025000 
PAXOH, 100,100,1,2718,27180000 
PAXMH,500,500,1,2244,561000000 
PISTF, 100,100,1,285,2850000 
MATTF.250,250,1,152,9500000 
POTTF.500,500.1.1939,484750000 
POTOH,500.500,1,3409,852250000 
POTMH, 1000,1000,1,5792,5792000000 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


D-38 


RPPTF.250,250,1.1719,107437500 
RPPOH,100,100,1,5358,53580000 
RPPMH,500,500,1,5929,1482250000 
CRRMH,250,250,1,1051,65687500 
PI AMH,250,250,1,3223,201437500 
MPNTF.50,50,1,6135.15337500 
MPNOH,100,100,1,3539,35390000 
PMKTF,50,50,1,11452,28630000 
PMKOH, 100,100,1,6668,66680000 
YRKMH,500,500,1,1102,275500000 
YRKPH,500,500,1,1603,400750000 
MOBPH,500,500,1,5370,1342500000 
APPTF, 100.100,1,151,1510000 
CHKOH.250,250,1,777,48562500 
JMSTF,250,250,1,4579,286187500 
JMSOH,500,500,1,1726,431500000 
JMSMH.l 000,1000,1,977,977000000 
JMSPH, 1000,1000,1,434,434000000 
WBEMH, 100,100,1,631,6310000 
SBEMH, 100,100,1,2773,27730000 
EBEMH,50,50,1,2584,6460000 
ELIMH, 100,100,1,5339,53390000 
LAFMH, 100,100,1,339,3390000 
ELIPH.500,500,1,246,61500000 
LYNPH, 100,100,1,1673,16730000 
Total Volume (m A 3) = 75199817500 


appendix d 


User Guide and Documentation for the Chesapeake Bay Interpolator 


E-1 


appendix 

Potential Methods for Assessing 
Shorter Duration Dissolved 
Oxygen Criteria 


POTENTIAL METHODS 

The 2003 Chesapeake Bay water-quality criteria document described three alternatives 
for assessing attainment of the short duration dissolved oxygen criteria (U.S. EPA 
2003). Those include: 1) logistic regression; 2) a time series statistical method and 3) 
continuous dissolved oxygen data collection using meters that are deployed for an 
extended period of time. Each of these approaches has strengths and drawbacks. 
Appropriate implementation of logistic regression or time series statistical methods 
may require continuous dissolved oxygen data. To develop the full capacity to assess 
the shorter duration dissolved oxygen criteria—7-day mean, 1-day mean and instanta¬ 
neous minimum, EPA recommends a phased approach in which the methods that are 
easiest to implement are employed initially while continuing to work on development 
and implementation of the more detailed and/or expensive methods. 

LOGISTIC REGRESSION 

The instantaneous minimum criteria imply the requirement that waters within the 
respective designated use be at or above the defined concentration everywhere all the 
time. Stated in this way, the logistic regression approach clearly has application to 
the challenge of assessing attainment of instantaneous minimum criteria. In the 
context of criteria attainment, logistic equations are developed from the long term 
dissolved oxygen data record, which predict the probability that the defined criteria 
concentrations were met, based on observed monthly mean concentration. 

The logistic regression approach utilizes a well-established statistical procedure 
(U.S. EPA 2004) and has been employed in the past in Chesapeake Bay to estimate 
instantaneous minima (Jordan et al. 1992). It is relatively simple to use and only 
requires regular updating to keep the predictive models relevant to current condi¬ 
tions. The limitation of this approach is that it is based on an extrapolation of the 
fixed-station data and is likely to have higher error than the other methods. 


appendix e 


Potential Methods for Assessing Shorter Duration Dissolved Oxygen Criteria 




The logistic regression approach could be also be adapted to assess attainment of the 
7-day and 1-day mean criteria components as well as other duration-specific criteria, 
where and when a body of observational data is available at frequencies relevant to 
the time frame. High frequency ‘buoy’ data sited at sentinel locations, where contin¬ 
uous records extend over days, weeks and months, would offer opportunities to 
develop logistic models of the relationship between exceedance/attainment and the 
temporal means. EPA recommends that this method be actively developed for 
possible employment for attainment assessments of the instantaneous minimum 
dissolved oxygen criteria (see next section for details) while additional high 
frequency data are collected and more complex, detailed methods described below 
are being developed. 

SPECTRAL ANALYSIS METHOD 

The time series approach utilizes a statistical procedure known as spectral analysis 
to synthesize a complete record of dissolved oxygen concentrations at short interval 
time steps over time. The synthetic record is developed using continuous measure¬ 
ment data from nearby locations to develop a model that predicts the short-interval 
variations in concentration. That model is combined with the long-term pattern of 
variability derived from data collected routinely, monthly to twice monthly, at the 
fixed-stations located in the assessment unit. The synthetic dissolved oxygen record 
can then be used in the same way that data collected using a continuous meter would 
be used. This time series approach has only been applied in a limited way to date and 
further development is needed in order for it to fully meet the needs of a publishable 
Chesapeake Bay dissolved oxygen criteria assessment methodology (see pages 183- 
185 in U.S. EPA 2003). EPA recommends that this development work proceed 
simultaneously with the development of the logistic regression and that the spectral 
analysis method replace the logistic regression in the future should it prove a more 
robust method. 

COLLECTION OF CONTINUOUS MEASURES OF DISSOLVED 
OXYGEN CONCENTRATION 

The most rigorous approach for assessing attainment of the high frequency dissolved 
oxygen criteria would be to collect continuous measures of dissolved oxygen 
concentration at representative locations and depths throughout each spatial assess¬ 
ment unit. The temporal and spatial density of such data would need to be sufficient 
to enable all of the dissolved oxygen criteria to be assessed simply by calculating 
means at the appropriate time scales (e.g. 30-day, 7-day, 1-day) or by observing 
violations of the instantaneous minimum criteria values. However, continuous 
collection of high frequency dissolved oxygen concentration in the Bay is expensive 
both in purchasing the equipment and maintaining it. It is also difficult or impossible 
to find sufficiently representative locations where the equipment can be affixed to 
buoys or fixed pilings. Finally, it is expensive and labor-intensive to maintain the 
equipment and sensor calibration once it is deployed due to the effects of weather, 
turbulence, biological fouling and human interferences (e.g. accidents, thefts). 
Nevertheless, the collection of at least some continuous dissolved oxygen data will 
be critical for use in the other two statistical analysis-based assessment methods 


appendix e 


Potential Methods for Assessing Shorter Duration Dissolved Oxygen Criteria 


described above. Therefore, EPA recommends that the States continue to seek funds 
to support this type of data collection in order to directly generate the data supporting 
attainment assessment of the full array of applicable dissolved oxygen criteria. 

APPLICATION OF LOGISTIC REGRESSION TO ASSESS SHORT- 
DURATION DISSOLVED OXYGEN CRITERIA COMPONENTS 

In the prior sections, it was noted that the data collection frequency of the long term, 
fixed-station water quality monitoring program is inadequate to assess attainment of 
short-duration criteria components. However, the greater than 20-year record of 
dissolved oxygen measurements collected relatively synoptically throughout the 
mainstem Bay, tidal tributaries and embayments, and collected regularly throughout 
the annual cycle provides a very substantial data base from which to derive infer¬ 
ences and define quantitative relationships between seasonal and monthly mean 
dissolved oxygen concentrations and the frequency of observations above and below 
specified criterion concentrations. Where relationships are strong, the logistic regres¬ 
sion procedure produces models in the form of simple equations that 
estimate/predict the likelihood that the criterion threshold concentration was attained 
or violated during the period. 

This method was explored originally to measure attainment of the 1992 Chesapeake 
Bay dissolved oxygen restoration goal (Jordan et al. 1992) and was adapted for 
assessing attainment of the 2003 Chesapeake Bay dissolved oxygen instantaneous 
minimum (see Chapter 5, pages 27-62, in U.S. EPA 2004). The 2003 method modi¬ 
fications included spatial and temporal refinements to the predictive models, with 
consequent improvements to their goodness of fit. The early (1992) models esti¬ 
mated exceedance based on segment-specific seasonal means and whether the means 
were from depths above or below pycnocline. The 2003 method update was enriched 
with an additional decade of monitoring data (1990-2000) for the regression analysis 
and provided segment-specific models for individual months and depths. Recent 
progress on this work again includes several additional years of new fixed-station 
and continuous monitoring buoy data (2001-2005) and modifications to implemen¬ 
tation procedures that could provide results for attainment assessment through the 
CFD methodology in a format consistent with other dissolved oxygen criteria. 

In this latest iteration, logistic regression models for the individual instantaneous 
minima are developed for each station. The independent variables are, as before, 
mean dissolved oxygen, month and water depth. The addition of a depth-squared 
variable for deep stations is being tested, but not yet implemented. The dependent 
variable is an indicator that the minimum threshold (e.g., the instantaneous criterion 
concentration) is violated. (Since the CFD methodology is based on percent failure, 
the dependent variable is based on exceedance rather than attainment.) This model¬ 
building step currently uses the entire 1985-2005 water quality data record at each 
station. Over time, however, if trends in ambient dissolved oxygen indicate signifi¬ 
cant, sustained change in a segment, then the extent of the historical record to be 
included in this step should be re-examined. 

The collection of station models is used to estimate a predicted probability of 
exceedance for each station, for each month in the 3-year, multi-month seasonal 
assessment period, at each meter of depth. Then, for each month, the predicted prob- 


appendix e • Potential Methods for Assessing Shorter Duration Dissolved Oxygen Criteria 


abilities are spatially interpolated to estimate probabilities for all interpolator cells 
that represent the bathymetry of the Bay, its tidal tributaries and embayments. The 
interpolator cells that are contained within the designated use where the criterion 
applies are parsed out by segment and the probabilities calculated for each cell are 
evaluated cell-by-cell against a threshold of probability which indicates an unac¬ 
ceptably high risk that the dissolved oxygen criterion was exceeded (Jordan et al 
1992). The volume of water represented by the interpolator cells exceeding the 
threshold as a percentage of the total volume in the designated use is tallied for each 
segment, for each month in the assessment period. 

There are several elements of the logistic regression approach which should be eval¬ 
uated as part of the attainment assessment procedure. Each of the station-specific 
logistic models has its own goodness-of-fit measure. Each station will have a result 
from the predictive model, i.e., the probability of exceeding the instantaneous 
minimum over the assessment unit. Each segment will have an estimate of the 
percent volume exceeding the criteria, based on spatial interpolation of the station 
probabilities. As with other components of the dissolved oxygen criteria, these 
results can also be assessed and visualized using the CFD methodology, although 
this is not mandatory. 

The limitations of this methodology have been noted earlier, particularly the 
temporal frequency on which the models are based. In addition, the lack of good 
spatial representation in the tidal tributaries and embayments is a concern. Most of 
the fixed-stations are situated more or less longitudinally in mid-channel and there 
is insufficient lateral coverage of the flanks, where different oxygen conditions and 
different model relationships may exist. Data now being collected through the 
Chesapeake Bay Shallow Monitoring Program will help answer where and to what 
extent this is true. 


LITERATURE CITED 

Jordan, J., C. Stenger, M. Olson, R. Batiuk and K. Mountford. 1992. Chesapeake Bay 
Dissolved Oxygen Goal for Restoration of Living Resource Habitats. CBP/TRS 88/93. 
Chesapeake Bay Program, Annapolis, Maryland. 

U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries. 
EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, Maryland. 


Potential Methods for Assessing Shorter Duration Dissolved Oxygen Criteria 



appendix 



Data Used in Deriving the 
Open-Water, Deep-Water and 
Deep-Channel Dissolved Oxygen 
Criteria Summer Biological 

Table F-1. Designated use, segment, year combinations found to be "good" using 
the Benthic-IBI summer reference curve area locator method described in 
Chapter 4. 


CBP Segment 

Year 

Designated Use 

CB6PH 

1985 

DW 

CB7PH 

1985 

OW 

CB8PH 

1985 

OW 

JMSPH 

1985 

OW 

LCHMH 

1985 

OW 

NANOH 

1985 

OW 

RPPOH 

1985 

OW 

CB6PH 

1986 

DW 

CB8PH 

1986 

OW 

CHOMH1 

1986 

OW 

CHSMH 

1986 

OW 

CHSMH 

1986 

DW 

CHSMH 

1986 

DC 

JMSOH 

1986 

OW 

JMSPH 

1986 

OW 

RPPOH 

1986 

OW 

YRKMH 

1986 

OW 

CB3MH 

1987 

DW 

CB6PH 

1987 

DW 

CB8PH 

1987 

OW 

CHOMH1 

1987 

OW 

CHSMH 

1987 

DW 

JMSOH 

1987 

OW 

JMSPH 

1987 

OW 

NANMH 

1987 

OW 

PMKTF 

1987 

OW 

RPPMH 

1987 

OW 

RPPMH 

1987 

DW 

RPPOH 

1987 

OW 

CB20H 

1988 

OW 


CBP Segment 

Year 

Designated Use 

CB7PH 

1988 

OW 

JMSMH 

1988 

OW 

JMSPH 

1988 

OW 

NANMH 

1988 

OW 

PAXMH 

1988 

OW 

PMKTF 

1988 

OW 

RPPMH 

1988 

OW 

YRKMH 

1988 

OW 

CB20H 

1989 

OW 

CB3MH 

1989 

DW 

CB8PH 

1989 

OW 

JMSPH 

1989 

OW 

POTMH 

1989 

OW 

CB1TF 

1990 

OW 

CB7PH 

1990 

OW 

CB8PH 

1990 

OW 

CHOOH 

1990 

OW 

CHSMH 

1990 

DW 

CHSOH 

1990 

OW 

JMSPH 

1990 

OW 

JMSTF 

1990 

OW 

PAXOH 

1990 

OW 

RPPMH 

1990 

OW 

CB6PH 

1991 

DW 

CB7PH 

1991 

OW 

CB8PH 

1991 

OW 

CHOMH2 

1991 

OW 

JMSMH 

1991 

OW 

JMSPH 

1991 

OW 

JMSTF 

1991 

OW 


appendix f • Data Used in Deriving Summer Biological Reference Curves 




CBP Segment 

Year 

Designated Use 

PMKTF 

1991 

OW 

POTMH 

1991 

DW 

RPPMH 

1991 

OW 

RPPMH 

1991 

DW 

CB1TF 

1992 

OW 

CB20H 

1992 

OW 

CB5MH 

1992 

OW 

CB6PH 

1992 

OW 

CB6PH 

1992 

DW 

CB8PH 

1992 

OW 

CHOTF 

1992 

OW 

CHSMH 

1992 

OW 

CHSMH 

1992 

DC 

CHSOH 

1992 

OW 

ELKOH 

1992 

OW 

JMSPH 

1992 

OW 

JMSTF 

1992 

OW 

PMKTF 

1992 

OW 

POTMH 

1992 

DW 

POTTF 

1992 

OW 

RPPMH 

1992 

OW 

SASOH 

1992 

OW 

CB3MH 

1993 

DW 

CB6PH 

1993 

OW 

CB6PH 

1993 

DW 

CB7PH 

1993 

OW 

CB8PH 

1993 

OW 

CHOMH2 

1993 

OW 

CHSMH 

1993 

DW 

CHSMH 

1993 

DC 

JMSPH 

1993 

OW 

JMSTF 

1993 

OW 

PMKTF 

1993 

OW 

CB20H 

1994 

OW 

CB5MH 

1994 

DW 

CB7PH 

1994 

OW 

CB8PH 

1994 

OW 

CHOMH2 

1994 

OW 

CHSMH 

1994 

OW 

HNGMH 

1994 

OW 

JMSMH 

1994 

OW 

JMSPH 

1994 

OW 

LCHMH 

1994 

OW 

PMKTF 

1994 

OW 

BSHOH 

1995 

OW 

CB1TF 

1995 

OW 

CB3MH 

1995 

OW 

CB6PH 

1995 

OW 

CB6PH 

1995 

DW 


CBP Segment 

Year 

Designated Use 

CB8PH 

1995 

OW 

JMSPH 

1995 

OW 

MIDOH 

1995 

OW 

NANMH 

1995 

OW 

PAXTF 

1995 

OW 

PMKTF 

1995 

OW 

RPPMH 

1995 

OW 

SASOH 

1995 

OW 

SEVMH 

1995 

OW 

SOUMH 

1995 

OW 

TANMH 

1995 

OW 

YRKPH 

1995 

DW 

CB7PH 

1996 

DW 

CB8PH 

1996 

OW 

CHOOH 

1996 

OW 

CHSMH 

1996 

DC 

FSBMH 

1996 

OW 

JMSPH 

1996 

OW 

LCHMH 

1996 

OW 

MIDOH 

1996 

OW 

MPNOH 

1996 

OW 

NANMH 

1996 

OW 

PMKOH 

1996 

OW 

RPPTF 

1996 

OW 

SASOH 

1996 

OW 

SEVMH 

1996 

OW 

WICMH 

1996 

OW 

WSTMH 

1996 

OW 

BIGMH 

1997 

OW 

CB3MH 

1997 

DW 

CB6PH 

1997 

DW 

CB8PH 

1997 

OW 

CHOMH2 

1997 

OW 

CHSOH 

1997 

OW 

FSBMH 

1997 

OW 

JMSTF 

1997 

OW 

MANMH 

1997 

OW 

MIDOH 

1997 

OW 

MPNTF 

1997 

OW 

NANMH 

1997 

OW 

RHDMH 

1997 

OW 

RPPTF 

1997 

OW 

SOUMH 

1997 

OW 

BIGMH 

1998 

OW 

CB3MH 

1998 

OW 

CB3MH 

1998 

DW 

CB4MH 

1998 

DW 

CB6PH 

1998 

OW 

CB6PH 

1998 

DW 


Data Used in Deriving Summer Biological Reference Curves 




F-3 


CBP Segment 

Year 

Designated Use 

CBP Segment 

Year 

Designated Use 

CB8PH 

1998 

OW 

YRKPH 

2000 

OW 

CHOMH2 

1998 

ow 

CB20H 

2001 

OW 

CHOOH 

1998 

OW 

CB3MH 

2001 

DC 

CHSMH 

1998 

ow 

CB6PH 

2001 

DW 

CHSMH 

1998 

DW 

CHSMH 

2001 

DW 

GUNOH 

1998 

OW 

ELKOH 

2001 

OW 

JMSPH 

1998 

OW 

FSBMH 

2001 

OW 

MPNOH 

1998 

ow 

HNGMH 

2001 

OW 

MPNTF 

1998 

ow 

MANMH 

2001 

OW 

PAXTF 

1998 

ow 

MOBPH 

2001 

OW 

POCOH 

1998 

ow 

PMKTF 

2001 

OW 

POTTF 

1998 

ow 

RPPTF 

2001 

OW 

RPPTF 

1998 

ow 

SASOH 

2001 

OW 

WICMH 

1998 

ow 

WICMH 

2001 

ow 

CB3MH 

1999 

DW 

CB20H 

2002 

OW 

CB4MH 

1999 

DW 

CB5MH 

2002 

DW 

CB6PH 

1999 

OW 

CB7PH 

2002 

DW 

CB7PH 

1999 

ow 

CHKOH 

2002 

OW 

CB7PH 

1999 

DW 

CHOMH1 

2002 

OW 

CB8PH 

1999 

OW 

CRRMH 

2002 

OW 

CHSMH 

1999 

OW 

NANOH 

2002 

OW 

CHSMH 

1999 

DC 

PAXTF 

2002 

OW 

JMSPH 

1999 

OW 

PMKTF 

2002 

OW 

JMSTF 

1999 

OW 

RPPOH 

2002 

ow 

LYNPH 

1999 

OW 

RPPTF 

2002 

ow 

POCMH 

1999 

OW 

YRKPH 

2002 

DW 

RHDMH 

1999 

ow 

BIGMH 

2003 

OW 

WICMH 

1999 

ow 

CB20H 

2003 

OW 

WSTMH 

1999 

ow 

CB6PH 

2003 

ow 

BSHOH 

2000 

ow 

CB8PH 

2003 

ow 

CB20H 

2000 

ow 

CHSOH 

2003 

ow 

CB7PH 

2000 

DW 

JMSPH 

2003 

ow 

CB8PH 

2000 

ow 

MIDOH 

2003 

ow 

CHKOH 

2000 

ow 

MPNOH 

2003 

ow 

CHSOH 

2000 

ow 

POCMH 

2003 

ow 

EASMH 

2000 

ow 

APPTF 

2004 

ow 

ELKOH 

2000 

ow 

BOHOH 

2004 

ow 

HNGMH 

2000 

ow 

CB1TF 

2004 

ow 

JMSPH 

2000 

ow 

CB20H 

2004 

ow 

JMSTF 

2000 

ow 

CB6PH 

2004 

ow 

LAFMH 

2000 

ow 

CB8PH 

2004 

ow 

MIDOH 

2000 

ow 

CHKOH 

2004 

ow 

MPNTF 

2000 

ow 

CHOMH1 

2004 

ow 

NANOH 

2000 

ow 

CHOTF 

2004 

ow 

PMKOH 

2000 

ow 

CHSMH 

2004 

ow 

PMKTF 

2000 

ow 

CHSOH 

2004 

ow 

POTOH 

2000 

ow 

CRRMH 

2004 

ow 

RPPTF 

2000 

ow 

GUNOH 

2004 

ow 

SEVMH 

2000 

ow 

MANMH 

2004 

ow 


appendix f • Data Used in Deriving Summer Biological Reference Curves 




CBP Segment 

Year 

Designated Use 

MPNTF 

2004 

OW 

NORTF 

2004 

ow 

RPPOH 

2004 

OW 

CB1TF 

2005 

ow 

CB7PH 

2005 

DW 

CHOMH2 

2005 

OW 

FSBMH 

2005 

OW 

PMKOH 

2005 

ow 

SASOH 

2005 

ow 

TANMH 

2005 

ow 


appendix f 


Data Used in Deriving Summer Biological Reference Curves 


appendix 


Equations for the Open-Water, 
Deep-Water and Deep-Channel 
Dissolved Oxygen Criteria 
Summer Biological Reference 

Curves 


A biological reference curve of acceptable violation rates is generated using a cumu¬ 
lative frequency distribution (CFD) of violation rates for “healthy” designated uses. 
The violation rates are sorted in ascending order, ranked in descending order, and 
graphed on a quantile plot: 

• Violation rates are plotted on the x-axis, with plotting position on the y-axis. 

• Plotting position represents the probability, i/n, of being less than or equal to a 
given violation rate, or x, and is plotted on the y-axis as a function of rank, or 
“i”, and sample size, or “n”. 

• The x-axis is labeled “Percentage of Volume” because the violation rate repre¬ 
sents the fraction of volume that is in violation. 

• The y-axis is labeled as “Percentage of Time” because “probability” represents 
the probable amount of time that a given violation rate will be observed. 

• The Chesapeake Bay Program currently uses the Wiebull plotting position to 
plot the cumulative distribution function. The Wiebull equation for calculating 
probability, y, for each violation rate with rank “i” is: y = i/(n+l); i = rank. 

In order to generate a graph of the CFD: 

• X|, x 2 , x 3 ,...x n = violation rates provided herein, sorted in ascending order, 
with rank (i) assigned in descending order. 

• yj = i/(n+l). 

• After plotting the data’s violation rates and probabilities, two additional points 
should be added to the distribution in order to complete the CFD curve: 

Insert (x 0 , yo) = (0,1) before the first data point; and 

Insert (x n+1 , y n+1 ) = (1,0) after the last data point. 


appendix g • Equations for the Summer Biological Reference Curves 


G*2 


f 

DEEP CHANNEL INSTANTANEOUS VALUES 


rank 

Fraction 

Volume 

Fraction 

Time 


0 

1 

39 

0 

0.975 

38 

0 

0.95 

37 

0 

0.925 

36 

0 

0.9 

35 

0 

0.875 

34 

0 

0.85 

33 

0 

0.825 

32 

0 

0.8 

31 

0 

0.775 

30 

0 

0.75 

29 

0 

0.725 

28 

0 

0.7 

27 

0 

0.675 

26 

0 

0.65 

25 

0 

0.625 

24 

0 

0.6 

23 

0 

0.575 

22 

0 

0.55 

21 

0 

0.525 

20 

0 

0.5 

19 

0 

0.475 

18 

0 

0.45 

17 

0 

0.425 

16 

0 

0.4 

15 

0 

0.375 

14 

0 

0.35 

13 

0.1229698 

0.325 

12 

0.1377778 

0.3 

11 

0.1869919 

0.275 

10 

0.192 

0.25 

9 

0.1938775 

0.225 

8 

0.2833333 

0.2 

7 

0.3069767 

0.175 

6 

0.3857374 

0.15 

5 

0.5 

0.125 

4 

0.6338462 

0.1 

3 

0.7984496 

0.075 

2 

1 

0.05 

1 

1 

0.025 


1 

0 


appendix g 


Equations for the Summer Biological Reference Curves 






G-3 


DEEP WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

111 

0 

0.711538462 

110 

0 

0.705128205 

109 

0 

0.698717949 

108 

0 

0.692307692 

107 

0 

0.685897436 

106 

0.0011772 

0.679487179 

105 

0.0027367 

0.673076923 

104 

0.0053908 

0.666666667 

103 

0.0058608 

0.66025641 

102 

0.0071155 

0.653846154 

101 

0.0082474 

0.647435897 

100 

0.0086758 

0.641025641 

99 

0.0105042 

0.634615385 

98 

0.0119522 

0.628205128 

97 

0.014231 

0.621794872 

96 

0.0143416 

0.615384615 

95 

0.015544 

0.608974359 

94 

0.0186097 

0.602564103 

93 

0.0186104 

0 596153846 

92 

0.0186916 

0.58974359 

91 

0.0229885 

0.583333333 

90 

0.0242872 

0.576923077 

89 

0.0290657 

0.570512821 

88 

0.0303867 

0.564102564 

87 

0.0341702 

0.557692308 

86 

0.0372195 

0.551282051 

85 

0.0394495 

0.544871795 

84 

0.0442319 

0.538461538 

83 

0.0468541 

0.532051282 

82 

0.0492611 

0.525641026 

81 

0.053407 

0.519230769 

80 

0.0596184 

0.512820513 

79 

0.0646766 

0.506410256 

78 

0.0669035 

0.5 

77 

0.0749625 

0.493589744 

76 

0.0772947 

0.487179487 

75 

0.0773381 

0.480769231 

74 

0.0819209 

0.474358974 

73 

0.0830704 

0.467948718 

72 

0.0842912 

0.461538462 

71 

0.0843786 

0.455128205 

70 

0.0914286 

0.448717949 

69 

0.0922064 

0.442307692 

68 

0.096124 

0.435897436 

67 

0.0967341 

0.429487179 


rank 

Fraction 

Volume 

Fraction Time 

155 

0 

0 

1 

0.993589744 

154 

0 

0.987179487 

153 

0 

0.980769231 

152 

0 

0.974358974 

151 

0 

0.967948718 

150 

0 

0.961538462 

149 

0 

0.955128205 

148 

0 

0.948717949 

147 

0 

0.942307692 

146 

0 

0.935897436 

145 

0 

0.929487179 

144 

0 

0.923076923 

143 

0 

0.916666667 

142 

0 

0.91025641 

141 

0 

0.903846154 

140 

0 

0.897435897 

139 

0 

0.891025641 

138 

0 

0.884615385 

137 

0 

0.878205128 

136 

0 

0.871794872 

135 

0 

0.865384615 

134 

0 

0.858974359 

133 

0 

0.852564103 

132 

0 

0.846153846 

131 

0 

0.83974359 

130 

0 

0.833333333 

129 

0 

0.826923077 

128 

0 

0.820512821 

127 

0 

0.814102564 

126 

0 

0.807692308 

125 

0 

0.801282051 

124 

0 

0.794871795 

123 

0 

0.788461538 

122 

0 

0.782051282 

121 

0 

0.775641026 

120 

0 

0.769230769 

119 

0 

0.762820513 

118 

0 

0.756410256 

117 

0 

0.75 

116 

0 

0.743589744 

115 

0 

0.737179487 

114 

0 

0.730769231 

113 

0 

0.724358974 

112 

0 

0.717948718 


appendix g 


Equations for the Summer Biological Reference Curves 











G-4 


DEEP WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

L 

Fraction Time 

66 

0.0986842 

0.423076923 

65 

0.1003289 

0.416666667 

64 

0.1030177 

0.41025641 

63 

0.1073883 

0.403846154 

62 

0.1123967 

0.397435897 

61 

0.1133005 

0.391025641 

60 

0.1142857 

0.384615385 

59 

0.1153846 

0.378205128 

58 

0.1340996 

0.371794872 

57 

0.1351351 

0.365384615 

56 

0.1405229 

0.358974359 

55 

0.1536643 

0.352564103 

54 

0.1561065 

0.346153846 

53 

0.1613475 

0.33974359 

52 

0.1666667 

0.333333333 

51 

0.1690574 

0.326923077 

50 

0.177641 

0.320512821 

49 

0.1888889 

0.314102564 

48 

0.193999 

0.307692308 

47 

0.2019704 

0.301282051 

46 

0.2030651 

0.294871795 

45 

0.2064298 

0.288461538 

44 

0.2138837 

0.282051282 

43 

0.2144487 

0.275641026 

42 

0.2149758 

0.269230769 

41 

0.2301587 

0.262820513 

40 

0.2398477 

0.256410256 

39 

0.2399356 

0.25 

38 

0.2473721 

0.243589744 

37 

0.2550629 

0.237179487 

36 

0.2568941 

0.230769231 

35 

0.2744511 

0.224358974 

34 

0.2754491 

0.217948718 


rank 

Fraction 

Volume 

Fraction Time 

33 

0.2863962 

0.211538462 

32 

0.2887439 

0.205128205 

31 

0.2992831 

0.198717949 

30 

0.304324 

0.192307692 

29 

0.3064989 

0.185897436 

28 

0.3065134 

0.179487179 

27 

0.3125 

0.173076923 

26 

0.313253 

0.166666667 

25 

0.3192771 

0.16025641 

24 

0.3256059 

0.153846154 

23 

0.3313559 

0.147435897 

22 

0.3367199 

0.141025641 

21 

0.3522608 

0.134615385 

20 

0.3867069 

0.128205128 

19 

0.4039409 

0.121794872 

18 

0.4058394 

0.115384615 

17 

0.4066776 

0.108974359 

16 

0.4071428 

0.102564103 

15 

0.4091904 

0.096153846 

14 

0.4172932 

0.08974359 

13 

0.4230019 

0.083333333 

12 

0.4251208 

0.076923077 

11 

0.4340449 

0.070512821 

10 

0.4419155 

0.064102564 

9 

0.4548346 

0.057692308 

8 

0.4548849 

0.051282051 

7 

0.4679803 

0.044871795 

6 

0.5176327 

0.038461538 

5 

0.5266618 

0.032051282 

4 

0.5465729 

0.025641026 

3 

0.5878661 

0.019230769 

2 

1 

0.012820513 

1 

1 

0.006410256 


1 

0 


appendix g 


Equations for the Summer Biological Reference Curves 










OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

822 

0 

0.945914845 

821 

0 

0.944764097 

820 

0 

0.943613349 

819 

0 

0.942462601 

818 

0 

0.941311853 

817 

0 

0.940161105 

816 

0 

0.939010357 

815 

0 

0.937859609 

814 

0 

0.936708861 

813 

0 

0.935558113 

812 

0 

0.934407365 

811 

0 

0.933256617 

810 

0 

0.932105869 

809 

0 

0.930955121 

808 

0 

0.929804373 

807 

0 

0.928653625 

806 

0 

0.927502877 

805 

0 

0.926352129 

804 

0 

0.925201381 

803 

0 

0.924050633 

802 

0 

0.922899885 

801 

0 

0.921749137 

800 

0 

0.920598389 

799 

0 

0.919447641 

798 

0 

0.918296893 

797 

0 

0.917146145 

796 

0 

0.915995397 

795 

0 

0.914844649 

794 

0 

0.913693901 

793 

0 

0.912543153 

792 

0 

0.911392405 

791 

0 

0.910241657 

790 

0 

0.909090909 

789 

0 

0.907940161 

788 

0 

0.906789413 

787 

0 

0.905638665 

786 

0 

0.904487917 

785 

0 

0.903337169 

784 

0 

0.902186421 

783 

0 

0.901035673 

782 

0 

0.899884925 

781 

0 

0.898734177 

780 

0 

0.897583429 

779 

0 

0.896432681 

778 

0 

0.895281933 

777 

0 

0.894131185 

776 

0 

0.892980437 


rank 

Fraction 

Volume 

Fraction Time 

868 

0 

0 

1 

0.998849252 

867 

0 

0.997698504 

866 

0 

0.996547756 

865 

0 

0.995397008 

864 

0 

0.99424626 

863 

0 

0.993095512 

862 

0 

0.991944764 

861 

0 

0.990794016 

860 

0 

0.989643268 

859 

0 

0.98849252 

858 

0 

0.987341772 

857 

0 

0.986191024 

856 

0 

0.985040276 

855 

0 

0.983889528 

854 

0 

0.98273878 

853 

0 

0.981588032 

852 

0 

0.980437284 

851 

0 

0.979286536 

850 

0 

0.978135788 

849 

0 

0.97698504 

848 

0 

0.975834292 

847 

0 

0.974683544 

846 

0 

0.973532796 

845 

0 

0.972382048 

844 

0 

0.9712313 

843 

0 

0.970080552 

842 

0 

0.968929804 

841 

0 

0.967779056 

840 

0 

0.966628308 

839 

0 

0.96547756 

838 

0 

0.964326812 

837 

0 

0.963176064 

836 

0 

0.962025316 

835 

0 

0.960874568 

834 

0 

0.95972382 

833 

0 

0.958573072 

832 

0 

0.957422325 

831 

0 

0.956271577 

830 

0 

0.955120829 

829 

0 

0.953970081 

828 

0 

0.952819333 

827 

0 

0.951668585 

826 

0 

0.950517837 

825 

0 

0.949367089 

824 

0 

0.948216341 

823 

0 

0.947065593 


appendix g 


Equations for the Summer Biological Reference Curves 












G-6 

/ 

OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 


rank 

Fraction 

Volume 

Fraction Time 

775 

0 

0.891829689 


727 

0 

0.836593786 

774 

0 

0.890678941 


726 

0 

0.835443038 

773 

0 

0.889528193 


725 

0 

0.83429229 

772 

0 

0.888377445 


724 

0 

0.833141542 

771 

0 

0.887226697 


723 

0 

0.831990794 

770 

0 

0.886075949 


722 

0 

0.830840046 

769 

0 

0.884925201 


721 

0 

0.829689298 

768 

0 

0.883774453 


720 

0 

0.82853855 

767 

0 

0.882623705 


719 

0 

0.827387802 

766 

0 

0.881472957 


718 

0 

0.826237054 

765 

0 

0.880322209 


717 

0 

0.825086306 

764 

0 

0.879171461 


716 

0 

0.823935558 

763 

0 

0.878020713 


715 

0 

0.82278481 

762 

0 

0.876869965 


714 

0 

0.821634062 

761 

0 

0.875719217 


713 

0 

0.820483314 

760 

0 

0.87456847 


712 

0 

0.819332566 

759 

0 

0.873417722 


711 

0 

0.818181818 

758 

0 

0.872266974 


710 

0 

0.81703107 

757 

0 

0.871116226 


709 

0 

0.815880322 

756 

0 

0.869965478 


708 

0 

0.814729574 

755 

0 

0.86881473 


707 

0 

0.813578826 

754 

0 

0.867663982 


706 

0 

0.812428078 

753 

0 

0.866513234 


705 

0 

0.81127733 

752 

0 

0.865362486 


704 

0 

0.810126582 

751 

0 

0.864211738 


703 

0 

0.808975834 

750 

0 

0.86306099 


702 

0 

0.807825086 

749 

0 

0.861910242 


701 

0 

0.806674338 

748 

0 

0.860759494 


700 

0 

0.80552359 

747 

0 

0.859608746 


699 

0 

0.804372842 

746 

0 

0.858457998 


698 

0 

0.803222094 

745 

0 

0.85730725 


697 

0 

0.802071346 

744 

0 

0.856156502 


696 

0 

0.800920598 

743 

0 

0.855005754 


695 

0 

0.79976985 

742 

0 

0.853855006 


694 

0 

0.798619102 

741 

0 

0.852704258 


693 

0 

0.797468354 

740 

0 

0.85155351 


692 

0 

0.796317606 

739 

0 

0.850402762 


691 

0 

0.795166858 

738 

0 

0.849252014 


690 

0 

0.79401611 

737 

0 

0.848101266 


689 

0 

0.792865362 

736 

0 

0.846950518 


688 

0 

0.791714614 

735 

0 

0.84579977 


687 

0 

0.790563867 

734 

0 

0.844649022 


686 

0 

0.789413119 

733 

0 

0.843498274 


685 

0 

0.788262371 

732 

0 

0.842347526 


684 

0 

0.787111623 

731 

0 

0.841196778 


683 

0 

0.785960875 

730 

0 

0.84004603 


682 

0 

0.784810127 

729 

0 

0.838895282 


681 

0 

0.783659379 

728 

0 

0.837744534 


680 

0 

0.782508631 


appendix g • Equations for the Summer Biological Reference Curves 















G*7 


OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

679 

0 

0.781357883 

678 

0 

0.780207135 

677 

0 

0.779056387 

676 

0 

0.777905639 

675 

0 

0.776754891 

674 

0 

0.775604143 

673 

0 

0.774453395 

672 

0 

0.773302647 

671 

0 

0.772151899 

670 

0 

0.771001151 

669 

0 

0.769850403 

668 

0 

0.768699655 

667 

0 

0.767548907 

666 

0 

0.766398159 

665 

0 

0.765247411 

664 

0 

0.764096663 

663 

0 

0.762945915 

662 

0 

0.761795167 

661 

0 

0.760644419 

660 

0 

0.759493671 

659 

0 

0.758342923 

658 

0 

0.757192175 

657 

0 

0.756041427 

656 

0 

0.754890679 

655 

0 

0.753739931 

654 

0 

0.752589183 

653 

0 

0.751438435 

652 

0 

0.750287687 

651 

0 

0.749136939 

650 

0 

0.747986191 

649 

0 

0.746835443 

648 

0 

0.745684695 

647 

0 

0.744533947 

646 

0 

0.743383199 

645 

0 

0.742232451 

644 

0 

0.741081703 

643 

0 

0.739930955 

642 

0 

0.738780207 

641 

0 

0.737629459 

640 

0 

0.736478711 

639 

0 

0.735327963 

638 

0 

0.734177215 

637 

0 

0.733026467 

636 

0 

0.731875719 

635 

0 

0.730724971 

634 

0 

0.729574223 

633 

0 

0.728423475 

632 

0 

0.727272727 


rank 

Fraction 

Volume 

Fraction Time 

631 

0 

0.726121979 

630 

0 

0.724971231 

629 

0 

0.723820483 

628 

0 

0.722669735 

627 

0 

0.721518987 

626 

0 

0.720368239 

625 

0 

0.719217491 

624 

0 

0.718066743 

623 

0 

0.716915995 

622 

0 

0.715765247 

621 

0 

0.714614499 

620 

0 

0.713463751 

619 

0 

0.712313003 

618 

0 

0.711162255 

617 

0 

0.710011507 

616 

0 

0.708860759 

615 

0 

0.707710012 

614 

0 

0.706559264 

613 

0 

0.705408516 

612 

0 

0.704257768 

611 

0 

0.70310702 

610 

0 

0.701956272 

609 

0 

0.700805524 

608 

0 

0.699654776 

607 

0 

0.698504028 

606 

0 

0.69735328 

605 

0 

0.696202532 

604 

0 

0.695051784 

603 

0 

0.693901036 

602 

0 

0.692750288 

601 

0 

0.69159954 

600 

0 

0.690448792 

599 

0 

0.689298044 

598 

0 

0.688147296 

597 

0 

0.686996548 

596 

0 

0.6858458 

595 

0 

0.684695052 

594 

0 

0.683544304 

593 

0 

0.682393556 

592 

0 

0.681242808 

591 

0 

0.68009206 

590 

0 

0.678941312 

589 

0 

0.677790564 

588 

0 

0.676639816 

587 

0 

0.675489068 

586 

0 

0.67433832 

585 

0 

0.673187572 

584 

0 

0.672036824 


appendix g • Equations for the Summer Biological Reference Curves 











G-8 

V 


OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

583 

0 

0.670886076 

582 

0 

0.669735328 

581 

0 

0.66858458 

580 

0 

0.667433832 

579 

0 

0.666283084 

578 

0 

0.665132336 

577 

0 

0.663981588 

576 

0 

0.66283084 

575 

0 

0.661680092 

574 

0 

0.660529344 

573 

0 

0.659378596 

572 

0 

0.658227848 

571 

0 

0.6570771 

570 

0 

0.655926352 

569 

0 

0.654775604 

568 

0 

0.653624856 

567 

0 

0.652474108 

566 

0 

0.65132336 

565 

0 

0.650172612 

564 

0 

0.649021864 

563 

0 

0.647871116 

562 

0 

0.646720368 

561 

0 

0.64556962 

560 

0 

0.644418872 

559 

0 

0.643268124 

558 

0 

0.642117376 

557 

0 

0.640966628 

556 

0 

0.63981588 

555 

0 

0.638665132 

554 

0 

0.637514384 

553 

0 

0.636363636 

552 

0 

0.635212888 

551 

0 

0.63406214 

550 

0 

0.632911392 

549 

0 

0.631760644 

548 

0 

0.630609896 

547 

0 

0.629459148 

546 

0 

0.6283084 

545 

0 

0.627157652 

544 

0 

0.626006904 

543 

0 

0.624856157 

542 

0 

0.623705409 

541 

0 

0.622554661 

540 

0 

0.621403913 

539 

0 

0.620253165 

538 

0 

0.619102417 

537 

0 

0.617951669 

536 

0 

0.616800921 


rank 

Fraction 

Volume 

Fraction Time 

535 

0 

0.615650173 

534 

0 

0.614499425 

533 

0 

0.613348677 

532 

0 

0.612197929 

531 

0 

0.611047181 

530 

0 

0.609896433 

529 

0 

0.608745685 

528 

0 

0.607594937 

527 

0 

0.606444189 

526 

0 

0.605293441 

525 

0 

0.604142693 

524 

0 

0.602991945 

523 

0 

0.601841197 

522 

0 

0.600690449 

521 

0 

0.599539701 

520 

0 

0.598388953 

519 

0 

0.597238205 

518 

0 

0.596087457 

517 

0 

0.594936709 

516 

0 

0.593785961 

515 

0 

0.592635213 

514 

0 

0.591484465 

513 

0 

0.590333717 

512 

0 

0.589182969 

511 

0 

0.588032221 

510 

0 

0.586881473 

509 

0 

0.585730725 

508 

0 

0.584579977 

507 

0 

0.583429229 

506 

0 

0.582278481 

505 

0 

0.581127733 

504 

0 

0.579976985 

503 

0 

0.578826237 

502 

0 

0.577675489 

501 

0 

0.576524741 

500 

0 

0.575373993 

499 

0 

0.574223245 

498 

0 

0.573072497 

497 

0 

0.571921749 

496 

0 

0.570771001 

495 

0 

0.569620253 

494 

0 

0.568469505 

493 

0 

0.567318757 

492 

0 

0.566168009 

491 

0 

0.565017261 

490 

0 

0.563866513 

489 

0 

0.562715765 

488 

0 

0 561565017 


appendix g 


Equations for the Summer Biological Reference Curves 













G-9 


OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

487 

0 

0.560414269 

486 

0 

0.559263521 

485 

0 

0.558112773 

484 

0 

0.556962025 

483 

0 

0.555811277 

482 

0 

0.554660529 

481 

0 

0.553509781 

480 

0 

0.552359033 

479 

0 

0.551208285 

478 

0 

0.550057537 

477 

0 

0.548906789 

476 

0 

0.547756041 

475 

0 

0.546605293 

474 

0 

0.545454545 

473 

0 

0.544303797 

472 

0 

0.543153049 

471 

0 

0.542002301 

470 

0 

0.540851554 

469 

0 

0.539700806 

468 

0 

0.538550058 

467 

0 

0.53739931 

466 

0 

0.536248562 

465 

0 

0.535097814 

464 

0 

0.533947066 

463 

0 

0.532796318 

462 

0 

0.53164557 

461 

0 

0.530494822 

460 

0 

0.529344074 

459 

0 

0.528193326 

458 

0 

0.527042578 

457 

0 

0.52589183 

456 

0 

0.524741082 

455 

0 

0 523590334 

454 

0 

0.522439586 

453 

0 

0.521288838 

452 

0 

0.52013809 

451 

0 

0.518987342 

450 

0 

0.517836594 

449 

0 

0.516685846 

448 

0 

0.515535098 

447 

0 

0.51438435 

446 

0 

0.513233602 

445 

0 

0.512082854 

444 

0 

0.510932106 

443 

0 

0.509781358 

442 

0 

0.50863061 

441 

0 

0.507479862 

440 

0 

0.506329114 


rank 

Fraction 

Volume 

Fraction Time 

439 

0 

0.505178366 

438 

0 

0.504027618 

437 

0 

0.50287687 

436 

0 

0.501726122 

435 

0 

0.500575374 

434 

0 

0.499424626 

433 

0 

0.498273878 

432 

0 

0.49712313 

431 

0 

0.495972382 

430 

0 

0.494821634 

429 

0 

0.493670886 

428 

0 

0.492520138 

427 

0 

0.49136939 

426 

0 

0.490218642 

425 

0 

0.489067894 

424 

0 

0.487917146 

423 

0 

0.486766398 

422 

0 

0.48561565 

421 

0 

0.484464902 

420 

0 

0.483314154 

419 

0 

0.482163406 

418 

0 

0.481012658 

417 

0 

0.47986191 

416 

0 

0.478711162 

415 

0 

0.477560414 

414 

0 

0.476409666 

413 

0 

0.475258918 

412 

0 

0.47410817 

411 

0 

0.472957422 

410 

0 

0.471806674 

409 

0 

0.470655926 

408 

0 

0.469505178 

407 

0 

0.46835443 

406 

0 

0.467203682 

405 

0 

0.466052934 

404 

0 

0.464902186 

403 

0 

0.463751438 

402 

0 

0.46260069 

401 

0 

0.461449942 

400 

0 

0.460299194 

399 

0 

0.459148446 

398 

0 

0.457997699 

397 

0 

0.456846951 

396 

0 

0.455696203 

395 

0 

0.454545455 

394 

0 

0.453394707 

393 

0 

0.452243959 

392 

0 

0.451093211 


appendix g 


Equations for the Summer Biological Reference Curves 










OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

391 

0 

0.449942463 

390 

0 

0.448791715 

389 

0 

0.447640967 

388 

0 

0.446490219 

387 

0 

0.445339471 

386 

0 

0.444188723 

385 

0 

0.443037975 

384 

0 

0.441887227 

383 

0 

0.440736479 

382 

0 

0.439585731 

381 

0 

0.438434983 

380 

0 

0.437284235 

379 

0 

0.436133487 

378 

0 

0.434982739 

377 

0 

0.433831991 

376 

0 

0.432681243 

375 

0 

0.431530495 

374 

0 

0.430379747 

373 

0 

0.429228999 

372 

0 

0.428078251 

371 

0 

0.426927503 

370 

0 

0.425776755 

369 

0.0005089 

0.424626007 

368 

0.0005304 

0.423475259 

367 

0.0005965 

0.422324511 

366 

0.0006126 

0.421173763 

365 

0.000801 

0.420023015 

364 

0.0009697 

0.418872267 

363 

0.0011871 

0.417721519 

362 

0.0014519 

0.416570771 

361 

0.0016221 

0.415420023 

360 

0.0016335 

0 414269275 

359 

0.0021645 

0.413118527 

358 

0.0021645 

0.411967779 

357 

0.0021684 

0.410817031 

356 

0.0022422 

0.409666283 

355 

0.0024594 

0.408515535 

354 

0.0024882 

0.407364787 

353 

0.0025445 

0.406214039 

352 

0.0029858 

0.405063291 

351 

0.0035562 

0.403912543 

350 

0.0042328 

0.402761795 

349 

0.0044582 

0.401611047 

348 

0.0048011 

0.400460299 

347 

0.0049478 

0.399309551 

346 

0.0050706 

0.398158803 

345 

0.0055944 

0.397008055 

344 

0.005598 

0.395857307 


rank 

Fraction 

Volume 

Fraction Time 

343 

0.005618 

0.394706559 

342 

0.0056497 

0.393555811 

341 

0.0056497 

0.392405063 

340 

0.0056497 

0.391254315 

339 

0.0057741 

0.390103567 

338 

0.0060024 

0.388952819 

337 

0.0064935 

0.387802071 

336 

0.0064935 

0.386651323 

335 

0.0064935 

0.385500575 

334 

0.0064935 

0.384349827 

333 

0.006993 

0.383199079 

332 

0.0072816 

0.382048331 

331 

0.0077864 

0.380897583 

330 

0.0081425 

0.379746835 

329 

0.0083485 

0.378596087 

328 

0.009176 

0.377445339 

327 

0.009772 

0 376294591 

326 

0.0100344 

0.375143843 

325 

0.0108696 

0.373993096 

324 

0.011236 

0.372842348 

323 

0.0112994 

0.3716916 

322 

0.0113636 

0.370540852 

321 

0.0122877 

0.369390104 

320 

0.0129206 

0.368239356 

319 

0.0133457 

0.367088608 

318 

0.0135135 

0.36593786 

317 

0.0135135 

0.364787112 

316 

0.0138099 

0.363636364 

315 

0.0141643 

0.362485616 

314 

0.0145985 

0.361334868 

313 

0.0152119 

0.36018412 

312 

0.0153473 

0.359033372 

311 

0.0163934 

0.357882624 

310 

0.0169367 

0.356731876 

309 

0.0173661 

0.355581128 

308 

0.0178716 

0.35443038 

307 

0.0181209 

0.353279632 

306 

0.0190476 

0.352128884 

305 

0.0195954 

0.350978136 

304 

0.0201126 

0.349827388 

303 

0.020657 

0.34867664 

302 

0.021121 

0.347525892 

301 

0.0213471 

0.346375144 

300 

0.0220096 

0.345224396 

299 

0.0222782 

0.344073648 

298 

0.0227457 

0.3429229 

297 

0.0228091 

0.341772152 

296 

0.0235199 

0.340621404 


appendix g • Equations for the Summer Biological Reference Curves 










6-11 


OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

295 

0.0238095 

0.339470656 

294 

0.0238095 

0.338319908 

293 

0.0238095 

0.33716916 

292 

0.0238095 

0.336018412 

291 

0.0238095 

0.334867664 

290 

0.0238095 

0.333716916 

289 

0.0242139 

0.332566168 

288 

0.0243704 

0.33141542 

287 

0.0248963 

0.330264672 

286 

0.0251828 

0.329113924 

285 

0.025533 

0.327963176 

284 

0.0257611 

0.326812428 

283 

0.025804 

0.32566168 

282 

0.02595 

0.324510932 

281 

0.0277429 

0.323360184 

280 

0.0282486 

0.322209436 

279 

0.0285016 

0.321058688 

278 

0.0300546 

0.31990794 

277 

0.0310473 

0.318757192 

276 

0.0312355 

0.317606444 

275 

0.0321381 

0.316455696 

274 

0.0321381 

0.315304948 

273 

0.0328082 

0.3141542 

272 

0.0341186 

0.313003452 

271 

0.0349206 

0.311852704 

270 

0.0363636 

0.310701956 

269 

0.0364963 

0.309551208 

268 

0.0366795 

0.30840046 

267 

0.0374777 

0.307249712 

266 

0.0380952 

0.306098964 

265 

0.0381803 

0.304948216 

264 

0.0391608 

0.303797468 

263 

0.0399501 

0.30264672 

262 

0.0408163 

0.301495972 

261 

0.0422037 

0.300345224 

260 

0.042328 

0.299194476 

259 

0.0423729 

0.298043728 

258 

0.0427173 

0.29689298 

257 

0.043956 

0.295742232 

256 

0.0444942 

0.294591484 

255 

0.0452794 

0.293440736 

254 

0.0464169 

0.292289988 

253 

0.0470397 

0.291139241 

252 

0.0486772 

0.289988493 

251 

0.0486772 

0.288837745 

250 

0.0492197 

0.287686997 

249 

0.0511788 

0.286536249 

248 

0.0519084 

0.285385501 


rank 

Fraction 

Volume 

Fraction Time 

247 

0.0521231 

0.284234753 

246 

0.0529101 

0.283084005 

245 

0.0529101 

0.281933257 

244 

0.0530612 

0.280782509 

243 

0.0536869 

0.279631761 

242 

0.0536869 

0.278481013 

241 

0.0536869 

0.277330265 

240 

0.0541126 

0.276179517 

239 

0.0541126 

0.275028769 

238 

0.0541126 

0.273878021 

237 

0.0541126 

0.272727273 

236 

0.0541126 

0.271576525 

235 

0.0579235 

0.270425777 

234 

0.0586084 

0.269275029 

233 

0.0587911 

0.268124281 

232 

0.0596465 

0.266973533 

231 

0.0604396 

0.265822785 

230 

0.0614422 

0.264672037 

229 

0.0618847 

0.263521289 

228 

0.0620364 

0.262370541 

227 

0.0625508 

0.261219793 

226 

0.0653824 

0.260069045 

225 

0.0658002 

0.258918297 

224 

0.0684039 

0.257767549 

223 

0.0695876 

0.256616801 

222 

0.0708995 

0.255466053 

221 

0.0713287 

0.254315305 

220 

0.0714866 

0.253164557 

219 

0.0717703 

0.252013809 

218 

0.0739236 

0.250863061 

217 

0.0756155 

0.249712313 

216 

0.0757415 

0.248561565 

215 

0.0760522 

0.247410817 

214 

0.0779468 

0.246260069 

213 

0.0781759 

0.245109321 

212 

0.0784933 

0.243958573 

211 

0.0793651 

0.242807825 

210 

0.0813397 

0.241657077 

209 

0.0836718 

0.240506329 

208 

0.0852341 

0.239355581 

207 

0.0852341 

0.238204833 

206 

0.0852341 

0.237054085 

205 

0.0858202 

0.235903337 

204 

0.0859692 

0.234752589 

203 

0.0862069 

0.233601841 

202 

0.0867133 

0.232451093 

201 

0.0889352 

0.231300345 

200 

0.0924318 

0.230149597 


appendix g 


Equations for the Summer Biological Reference Curves 












G-12 


OPEN WATER MONTHLY VALUES 


Fraction 

Volume 

Fraction Time 


rank 

Fraction 

Volume 

Fraction Time 

0.0926076 

0.228998849 


151 

0.1631854 

0.173762946 

0.094402 

0.227848101 


150 

0.1643766 

0.172612198 

0.0951807 

0.226697353 


149 

0.1650579 

0.17146145 

0.0953661 

0.225546605 


148 

0.1661578 

0.170310702 

0.0980392 

0.224395857 


147 

0.1686848 

0.169159954 

0.0986222 

0.223245109 


146 

0.1688742 

0.168009206 

0.0995439 

0.222094361 


145 

0.1706892 

0.166858458 

0.1013514 

0.220943613 


144 

0.1722272 

0.16570771 

0.1056534 

0.219792865 


143 

0.1731602 

0.164556962 

0.1097062 

0.218642117 


142 

0.1735369 

0.163406214 

0.1108631 

0.217491369 


141 

0.1756757 

0.162255466 

0.1108631 

0.216340621 


140 

0.1779041 

0.161104718 

0.1110075 

0.215189873 


139 

0.1805116 

0.15995397 

0.1119293 

0.214039125 


138 

0.1805379 

0.158803222 

0.1123596 

0.212888377 


137 

0.1830601 

0.157652474 

0.1135513 

0.211737629 


136 

0.190725 

0.156501726 

0.113798 

0.210586881 


135 

0.1914525 

0.155350978 

0.1141304 

0.209436133 


134 

0.1941337 

0.15420023 

0.1160355 

0.208285386 


133 

0.199403 

0.153049482 

0.1222826 

0.207134638 


132 

0.201087 

0.151898734 

0.1235521 

0.20598389 


131 

0.2013652 

0.150747986 

0.1259259 

0.204833142 


130 

0.2039852 

0.149597238 

0.1260344 

0.203682394 


129 

0.2189781 

0.14844649 

0.1269841 

0.202531646 


128 

0.227972 

0.147295742 

0.1270358 

0.201380898 


127 

0.2337085 

0.146144994 

0.1300254 

0.20023015 


126 

0.2359882 

0.144994246 

0.1310766 

0.199079402 


125 

0.2374406 

0.143843498 

0.1316527 

0.197928654 


124 

0.2409669 

0.14269275 

0.1342461 

0.196777906 


123 

0.2419833 

0.141542002 

0.1372868 

0.195627158 


122 

0.2432432 

0.140391254 

0.1389115 

0.19447641 


121 

0.2444856 

0.139240506 

0.14 

0.193325662 


120 

0.2445605 

0.138089758 

0.14 

0.192174914 


119 

0.2457132 

0.13693901 

0.140647 

0.191024166 


118 

0.2472826 

0.135788262 

0.1415645 

0.189873418 


117 

0.2478753 

0.134637514 

0.1419069 

0.18872267 


116 

0.2583187 

0.133486766 

0.1435523 

0.187571922 


115 

0.2593284 

0.132336018 

0.1449735 

0.186421174 


114 

0.2593284 

0.13118527 

0.1455978 

0.185270426 


113 

0.2593284 

0.130034522 

0.1514339 

0.184119678 


112 

0.2611517 

0.128883774 

0.1538896 

0.18296893 


111 

0.2736842 

0.127733026 

0.1542142 

0.181818182 


110 

0.2866706 

0.126582278 

0.1566434 

0.180667434 


109 

0.2889755 

0.12543153 

0.1587452 

0.179516686 


108 

0.2900763 

0.124280783 

0.1595922 

0.178365938 


107 

0.2905174 

0.123130035 

0.1611479 

0.17721519 


106 

0.3018642 

0.121979287 

0.1614429 

0.176064442 


105 

0.3043062 

0.120828539 

0.162963 

0.174913694 


104 

0.3130724 

0.119677791 


appendix g 


Equations for the Summer Biological Reference Curves 










OPEN WATER MONTHLY VALUES 


G-13 

- 


rank 

Fraction 

Volume 

Fraction Time 

103 

0.319855 

0.118527043 

102 

0.3289125 

0.117376295 

101 

0.3301158 

0.116225547 

100 

0.3326345 

0.115074799 

99 

0.3344764 

0.113924051 

98 

0.350166 

0.112773303 

97 

0.350417 

0.111622555 

96 

0.3507276 

0.110471807 

95 

0.3753799 

0.109321059 

94 

0.3762215 

0.108170311 

93 

0.37668 

0.107019563 

92 

0.3773917 

0.105868815 

91 

0.3793436 

0.104718067 

90 

0.3807623 

0.103567319 

89 

0.3879781 

0.102416571 

88 

0.3998882 

0.101265823 

87 

0.4010152 

0.100115075 

86 

0.4038889 

0.098964327 

85 

0.4038889 

0.097813579 

84 

0.4038889 

0.096662831 

83 

0.4038889 

0.095512083 

82 

0.4038889 

0.094361335 

81 

0.4146816 

0.093210587 

80 

0.4163347 

0.092059839 

79 

0.4233177 

0.090909091 

78 

0.4269663 

0.089758343 

77 

0.429676 

0.088607595 

76 

0.4300699 

0.087456847 

75 

0.4304933 

0.086306099 

74 

0.4374046 

0.085155351 

73 

0.4423898 

0.084004603 

72 

0.4565826 

0.082853855 

71 

0.456869 

0.081703107 

70 

0.4570895 

0.080552359 

69 

0.4570895 

0.079401611 

68 

0.4570895 

0.078250863 

67 

0.464702 

0.077100115 

66 

0.4692463 

0.075949367 

65 

0.4841679 

0.074798619 

64 

0.5085324 

0.073647871 

63 

0.5108851 

0.072497123 

62 

0.5263544 

0.071346375 

61 

0.545568 

0.070195627 

60 

0.5625 

0.069044879 

59 

0.5701425 

0.067894131 

58 

0.575 

0.066743383 

57 

0.576076 

0.065592635 

56 

0.6036122 

0.064441887 


rank 

Fraction 

Volume 

Fraction Time 

55 

0.6200294 

0.063291139 

54 

0.6237785 

0.062140391 

53 

0.6320347 

0.060989643 

52 

0.6437941 

0.059838895 

51 

0.6469252 

0.058688147 

50 

0.6567556 

0.057537399 

49 

0.663745 

0.056386651 

48 

0.7578948 

0.055235903 

47 

0.7717455 

0.054085155 

46 

0.8227364 

0.052934407 

45 

0.8384528 

0.051783659 

44 

0.97 

0.050632911 

43 

0.9949544 

0.049482163 

42 

1 

0.048331415 

41 

1 

0.047180667 

40 

1 

0.046029919 

39 

1 

0.044879171 

38 

1 

0.043728423 

37 

1 

0.042577675 

36 

1 

0.041426928 

35 

1 

0.04027618 

34 

1 

0.039125432 

33 

1 

0.037974684 

32 

1 

0.036823936 

31 

1 

0.035673188 

30 

1 

0.03452244 

29 

1 

0.033371692 

28 

1 

0.032220944 

27 

1 

0.031070196 

26 

1 

0.029919448 

25 

1 

0.0287687 

24 

1 

0.027617952 

23 

1 

0.026467204 

22 

1 

0.025316456 

21 

1 

0.024165708 

20 

1 

0.02301496 

19 

1 

0.021864212 

18 

1 

0.020713464 

17 

1 

0.019562716 

16 

1 

0.018411968 

15 

1 

0.01726122 

14 

1 

0.016110472 

13 

1 

0.014959724 

12 

1 

0.013808976 

11 

1 

0.012658228 

10 

1 

0.01150748 

9 

1 

0.010356732 

8 

1 

0.009205984 


appendix cj 


Equations for the Summer Biological Reference Curves 












G-14 

' 


OPEN WATER MONTHLY VALUES 


rank 

Fraction 

Volume 

Fraction Time 

7 

1 

0.008055236 

6 

1 

0.006904488 

5 

1 

0.00575374 

4 

1 

0.004602992 

3 

1 

0.003452244 

2 

1 

0.002301496 

1 

1 

0.001150748 


1 

0 


appendix g 


Equations for the Summer Biological Reference Curves 






appendix 


H-1 

'-■t 


Equations for the 
Water Clarity Criteria 
Biological Reference Curves 


A biological reference curve of acceptable violation rates is generated using a cumu¬ 
lative frequency distribution (CFD) of violation rates for “healthy” designated uses. 
The violation rates are sorted in ascending order, ranked in descending order, and 
graphed on a quantile plot: 

• Violation rates are plotted on the x-axis, with plotting position on the y axis. 

• Plotting position represents the probability, i/n, of being less than or equal to a 
given violation rate, or x, and is plotted on the y-axis as a function of rank, or 
“i”, and sample size, or “n”. 

• The x-axis is labeled “space” because the violation rate represents the fraction 
of volume that is in violation. 

• The y-axis is labeled as “time” because “probability” represents the probable 
amount of time that a given violation rate will be observed. 

• The Chesapeake Bay Program currently uses the Wiebull plotting position to 
plot the cumulative distribution function. The Wiebull equation for calculating 
probability, y, for each violation rate with rank “i” is: y = i/(n+l); i = rank. 

In order to generate a graph of the CFD: 

• X] , x 2 , x 3 ,...x n = violation rates provided herein, sorted in ascending order, 
with rank (i) assigned in descending order. 

• y, = i/(n+l). 

• After plotting the data’s violation rates and probabilities, two additional points 
should be added to the distribution in order to complete the CFD curve: 

Insert (x 0 , y 0 ) = (0,1) before the first data point; and 
Insert (x n+1 , y n+1 ) = (1,0) after the last data point. 


appendix h • Equations for the Water Clarity Criteria Biological Reference Curves 


H*2 




TIDAL FRESH/OLIGOHALINE 


rank 

volume 

time 

406 

0 

0 

1 

0.997542998 

405 

0 

0.995085995 

404 

0 

0.992628993 

403 

0 

0.99017199 

402 

0 

0 987714988 

401 

0 

0.985257985 

400 

0 

0.982800983 

399 

0 

0.98034398 

398 

0 

0.977886978 

397 

0 

0.975429975 

396 

0 

0.972972973 

395 

0 

0.970515971 

394 

0 

0.968058968 

393 

0 

0.965601966 

392 

0 

0.963144963 

391 

0 

0.960687961 

390 

0 

0.958230958 

389 

0 

0.955773956 

388 

0 

0.953316953 

387 

0 

0.950859951 

386 

0 

0.948402948 

385 

0 

0.945945946 

384 

0 

0.943488943 

383 

0 

0.941031941 

382 

0 

0.938574939 

381 

0 

0.936117936 

380 

0 

0.933660934 

379 

0 

0.931203931 

378 

0 

0.928746929 

377 

0 

0.926289926 

376 

0 

0.923832924 

375 

0 

0.921375921 

374 

0 

0.918918919 

373 

0 

0.916461916 

372 

0 

0.914004914 

371 

0 

0.911547912 

370 

0 

0.909090909 

369 

0 

0.906633907 

368 

0 

0.904176904 

367 

0 

0.901719902 

366 

0 

0.899262899 

365 

0 

0 896805897 

364 

0 

0.894348894 

363 

0 

0.891891892 

362 

0 

0 889434889 

361 

0 

0 886977887 

360 

0 

0.884520885 

359 

0 

0 882063882 


rank 

volume 

time 

358 

0 

087960688 

357 

0 

0.877149877 

356 

0 

0.874692875 

355 

0 

0.872235872 

354 

0 

0.86977887 

353 

0 

0.867321867 

352 

0 

0.864864865 

351 

0 

0 862407862 

350 

0 

0.85995086 

349 

0 

0.857493857 

348 

0 

0.855036855 

347 

0 

0.852579853 

346 

0 

0.85012285 

345 

0 

0.847665848 

344 

0 

0.845208845 

343 

0 

0.842751843 

342 

0 

0.84029484 

341 

0 

0.837837838 

340 

0 

0.835380835 

339 

0 

0.832923833 

338 

0 

0.83046683 

337 

0 

0.828009828 

336 

0 

0.825552826 

335 

0 

0.823095823 

334 

0 

0 820638821 

333 

0 

0.818181818 

332 

0 

0.815724816 

331 

0 

0.813267813 

330 

0 

0 810810811 

329 

0 

0 808353808 

328 

0 

0.805896806 

327 

0 

0.803439803 

326 

0 

0.800982801 

325 

0 

0.798525799 

324 

0 

0.796068796 

323 

0 

0.793611794 

322 

0 

0.791154791 

321 

0 

0 788697789 

320 

0 

0.786240786 

319 

0 

0.783783784 

318 

0 

0.781326781 

317 

0 

0 778869779 

316 

0 

0.776412776 

315 

0 

0.773955774 

314 

0 

0.771498771 

313 

0 

0.769041769 

312 

0 

0.766584767 

311 

0 

0.764127764 

310 

0 

0.761670762 

309 

0 

0.759213759 

308 

0 

0.756756757 


appendix h 


Equations for the Water Clarity Criteria Biological Reference Curves 











TIDAL FRESH/OLIGOHALINE 


rank 

volume 

time 

307 

0 

0.754299754 

306 

0 

0.751842752 

305 

0 

0.749385749 

304 

0 

0 746928747 

303 

0 

0.744471744 

302 

0 

0 742014742 

301 

0 

0.73955774 

300 

0 

0.737100737 

299 

0 

0.734643735 

298 

0 

0.732186732 

297 

0 

0.72972973 

296 

0 

0.727272727 

295 

0 

0.724815725 

294 

0 

0.722358722 

293 

0 

0.71990172 

292 

0 

0.717444717 

291 

0 

0.714987715 

290 

0 

0.712530713 

289 

0 

0.71007371 

288 

0 

0.707616708 

287 

0 

0.705159705 

286 

0 

0.702702703 

285 

0 

0.7002457 

284 

0 

0 697788698 

283 

0 

0.695331695 

282 

0 

0.692874693 

281 

0 

0.69041769 

280 

0 

0687960688 

279 

0 

0.685503686 

278 

0 

0.683046683 

277 

0 

0.680589681 

276 

0 

0.678132678 

275 

0 

0.675675676 

274 

0 

0.673218673 

273 

0 

0 670761671 

272 

0 

0.668304668 

271 

0 

0 665847666 

270 

0 

0 663390663 

269 

0 

0.660933661 

268 

0 

0.658476658 

267 

0 

0.656019656 

266 

0 

0 653562654 

265 

0 

0.651105651 

264 

0 

0.648648649 

263 

0 

0 646191646 

262 

0 

0.643734644 

261 

0 

0.641277641 

260 

0 

0 638820639 

259 

0 

0.636363636 

258 

0 

0.633906634 

257 

0 

0 631449631 


rank 

volume 

time 

256 

0 

0 628992629 

255 

0 

0.626535627 

254 

0 

0.624078624 

253 

0 

0 621621622 

252 

0 

0.619164619 

251 

0 

0.616707617 

250 

0 

0.614250614 

249 

0 

0.611793612 

248 

0 

0.609336609 

247 

0 

0.606879607 

246 

0 

0.604422604 

245 

0 

0.601965602 

244 

0 

0.5995086 

243 

0 

0.597051597 

242 

0 

0.594594595 

241 

0 

0.592137592 

240 

0 

0.58968059 

239 

0 

0.587223587 

238 

0 

0.584766585 

237 

0 

0.582309582 

236 

0 

0.57985258 

235 

0 

0.577395577 

234 

0 

0.574938575 

233 

0 

0.572481572 

232 

0 

0 57002457 

231 

0 

0.567567568 

230 

0 

0.565110565 

229 

0 

0.562653563 

228 

0 

0 56019656 

227 

0 

0.557739558 

226 

0 

0.555282555 

225 

0 

0.552825553 

224 

0 

0 55036855 

223 

0 

0.547911548 

222 

0 

0 545454545 

221 

0 

0.542997543 

220 

0 

0.540540541 

219 

0 

0.538083538 

218 

0 

0.535626536 

217 

0 

0.533169533 

216 

0 

0.530712531 

215 

0 

0.528255528 

214 

0 

0.525798526 

213 

0 

0.523341523 

212 

0 

0.520884521 

211 

0 

0.518427518 

210 

0 

0.515970516 

209 

0 

0.513513514 

208 

0 

0.511056511 

207 

0 

0.508599509 

206 

0 

0.506142506 


appendix h • Equations for the Water Clarity Criteria Biological Reference Curves 










TIDAL FRESH/OLIGOHALINE 


H-4 

/y 

t 


rank 

volume 

time 


rank 

volume 

time 

205 

0 

0.503685504 


154 

0.0054 

0.378378378 

204 

0 

0.501228501 


153 

0.0108 

0 375921376 

203 

0 

0.498771499 


152 

0.0108 

0.373464373 

202 

0 

0.496314496 


151 

0.0108 

0.371007371 

201 

0 

0.493857494 


150 

0.0108 

0.368550369 

200 

0 

0.491400491 


149 

0.0108 

0 366093366 

199 

0 

0.488943489 


148 

0.0196 

0.363636364 

198 

0 

0.486486486 


147 

0.0215 

0.361179361 

197 

0 

0.484029484 


146 

0.0215 

0.358722359 

196 

0 

0.481572482 


145 

0.0215 

0.356265356 

195 

0 

0.479115479 


144 

0.0215 

0.353808354 

194 

0 

0.476658477 


143 

0.0215 

0.351351351 

193 

0 

0.474201474 


142 

0.0261 

0.348894349 

192 

0 

0.471744472 


141 

0.0269 

0.346437346 

191 

0 

0.469287469 


140 

0.0269 

0.343980344 

190 

0 

0.466830467 


139 

0.0278 

0.341523342 

189 

0 

0.464373464 


138 

0.0278 

0.339066339 

188 

0 

0.461916462 


137 

0.0278 

0.336609337 

187 

0 

0.459459459 


136 

0.0323 

0.334152334 

186 

0 

0.457002457 


135 

0.0323 

0.331695332 

185 

0 

0.454545455 


134 

0.0455 

0.329238329 

184 

0 

0.452088452 


133 

0.0556 

0.326781327 

183 

0 

0.44963145 


132 

0.0719 

0.324324324 

182 

0 

0.447174447 


131 

0.0784 

0.321867322 

181 

0 

0.444717445 


130 

0.1111 

0.319410319 

180 

0 

0.442260442 


129 

0.1176 

0.316953317 

179 

0 

0.43980344 


128 

0.1237 

0.314496314 

178 

0 

0.437346437 


127 

0.1307 

0.312039312 

177 

0 

0.434889435 


126 

0.1307 

0.30958231 

176 

0 

0.432432432 


125 

0.1389 

0.307125307 

175 

0 

0.42997543 


124 

0.1389 

0.304668305 

174 

0 

0.427518428 


123 

0.1389 

0.302211302 

173 

0 

0.425061425 


122 

0.1389 

0.2997543 

172 

0 

0.422604423 


121 

0.1438 

0.297297297 

171 

0 

0.42014742 


120 

0.1505 

0.294840295 

170 

0 

0.417690418 


119 

0.1667 

0.292383292 

169 

0 

0 415233415 


118 

0.1667 

0.28992629 

168 

0 

0.412776413 


117 

0.1944 

0.287469287 

167 

0 

0.41031941 


116 

0.1989 

0.285012285 

166 

0 

0.407862408 


115 

0.2151 

0.282555283 

165 

0 

0.405405405 


114 

0.2222 

0.28009828 

164 

0 

0.402948403 


113 

0.25 

0.277641278 

163 

0 

0.4004914 


112 

0.2742 

0.275184275 

162 

0 

0.398034398 


111 

0.2778 

0.272727273 

161 

0 

0.395577396 


110 

0.3116 

0.27027027 

160 

0 

0 393120393 


109 

0.3203 

0.267813268 

159 

0 

0.390663391 


108 

0.3659 

0.265356265 

158 

0 

0.388206388 


107 

0.3889 

0 262899263 

157 

0 

0.385749386 


106 

0.3889 

0.26044226 

156 

0 

0.383292383 


105 

0.4167 

0.257985258 

155 

0 

0 380835381 


104 

0.4167 

0.255528256 


appendix h 


Equations for the Water Clarity Criteria Biological Reference Curves 














H-5 


TIDAL FRESH/OLIGOHALINE 


rank 

volume 

time 

103 

0.4444 

0.253071253 

102 

0.4444 

0.250614251 

101 

0.5538 

0.248157248 

100 

0.5581 

0.245700246 

99 

0.5752 

0.243243243 

98 

0.5948 

0.240786241 

97 

0.6136 

0.238329238 

96 

0.6136 

0.235872236 

95 

0.6202 

0.233415233 

94 

0.6237 

0.230958231 

93 

0.6434 

0.228501229 

92 

0.6434 

0.226044226 

91 

0.6434 

0.223587224 

90 

0.6434 

0.221130221 

89 

0.6899 

0.218673219 

88 

0.7209 

0.216216216 

87 

0.7209 

0.213759214 

86 

0.7364 

0 211302211 

85 

0.7519 

0.208845209 

84 

0.7597 

0.206388206 

83 

0.7597 

0.203931204 

82 

0.7674 

0.201474201 

81 

0.7727 

0.199017199 

80 

0.7796 

0.196560197 

79 

0.7974 

0.194103194 

78 

0.8062 

0.191646192 

77 

0.8188 

0.189189189 

76 

0.8225 

0.186732187 

75 

0.8225 

0.184275184 

74 

0 8333 

0.181818182 

73 

0.837 

0.179361179 

72 

0.8611 

0.176904177 

71 

0.8864 

0.174447174 

70 

0 8864 

0.171990172 

69 

0 8889 

0.16953317 

68 

0.8992 

0.167076167 

67 

0.8992 

0.164619165 

66 

0 8992 

0.162162162 

65 

0 9058 

0.15970516 

64 

0.938 

0.157248157 

63 

0 9457 

0.154791155 

62 

0.9477 

0.152334152 

61 

0.9545 

0.14987715 

60 

0.9545 

0.147420147 

59 

0.9612 

0.144963145 

58 

0 9674 

0.142506143 

57 

0.9722 

0.14004914 

56 

0.9739 

0.137592138 

55 

0.9767 

0135135135 

54 

0.9767 

0.132678133 

53 

0.9773 

0.13022113 


rank 

volume 

time 

52 

0.9804 

0.127764128 

51 

0.9804 

0.125307125 

50 

0.9804 

0.122850123 

49 

0.9935 

0.12039312 

48 

0.9935 

0.117936118 

47 

1 

0.115479115 

46 

1 

0.113022113 

45 

1 

0.110565111 

44 

1 

0.108108108 

43 

1 

0.105651106 

42 

1 

0.103194103 

41 

1 

0.100737101 

40 

1 

0.098280098 

39 

1 

0.095823096 

38 

1 

0.093366093 

37 

1 

0.090909091 

36 

1 

0.088452088 

35 

1 

0.085995086 

34 

1 

0.083538084 

33 

1 

0.081081081 

32 

1 

0.078624079 

31 

1 

0.076167076 

30 

1 

0.073710074 

29 

1 

0.071253071 

28 

1 

0.068796069 

27 

1 

0.066339066 

26 

1 

0.063882064 

25 

1 

0.061425061 

24 

1 

0.058968059 

23 

1 

0.056511057 

22 

1 

0 054054054 

21 

1 

0.051597052 

20 

1 

0.049140049 

19 

1 

0.046683047 

18 

1 

0.044226044 

17 

1 

0.041769042 

16 

1 

0.039312039 

15 

1 

0.036855037 

14 

1 

0.034398034 

13 

1 

0.031941032 

12 

1 

0.029484029 

11 

1 

0.027027027 

10 

1 

0.024570025 

9 

1 

0.022113022 

8 

1 

0.01965602 

7 

1 

0.017199017 

6 

1 

0.014742015 

5 

1 

0.012285012 

4 

1 

0.00982801 

3 

1 

0.007371007 

2 

1 

0.004914005 


appendix h • Equations for the Water Clarity Criteria Biological Reference Curves 














TIDAL FRESH/OUGOHALINE 


rank 

volume 

time 


rank 

volume 

time 

1 

1 

0.002457002 


347 

0 

0.887468031 


1 

0 


346 

0 

0.884910486 




345 

0 

0 882352941 




344 

0 

0.879795396 

MESOHAUNE/POLYH ALINE 

343 

0 

0 877237852 




342 

0 

0 874680307 

rank 

volume 

time 


341 

0 

0.872122762 


0 

1 


340 

0 

0.869565217 

390 

0 

0.997442455 


339 

0 

0.867007673 

389 

0 

099488491 


338 

0 

0.864450128 

388 

0 

0 992327366 


337 

0 

0.861892583 

387 

0 

0.989769821 


336 

0 

0.859335038 

386 

0 

0.987212276 


335 

0 

0.856777494 

385 

0 

0.984654731 


334 

0 

0.854219949 

384 

0 

0.982097187 


333 

0 

0.851662404 

383 

0 

0.979539642 


332 

0 

0.849104859 

382 

0 

0.976982097 


331 

0 

0 846547315 

381 

0 

0.974424552 


330 

0 

0.84398977 

380 

0 

0.971867008 


329 

0 

0.841432225 

379 

0 

0.969309463 


328 

0 

0.83887468 

378 

0 

0.966751918 


327 

0 

0.836317136 

377 

0 

0.964194373 


326 

0 

0 833759591 

376 

0 

0.961636829 


325 

0 

0.831202046 

375 

0 

0.959079284 


324 

0 

0.828644501 

374 

0 

0.956521739 


323 

0 

0.826086957 

373 

0 

0.953964194 


322 

0 

0.823529412 

372 

0 

0.95140665 


321 

0 

0.820971867 

371 

0 

0.948849105 


320 

0 

0.818414322 

370 

0 

0.94629156 


319 

0 

0 815856777 

369 

0 

0.943734015 


318 

0 

0.813299233 

368 

0 

0 941176471 


317 

0 

0.810741688 

367 

0 

0.938618926 


316 

0 

0.808184143 

366 

0 

0.936061381 


315 

0 

0.805626598 

365 

0 

0 933503836 


314 

0 

0 803069054 

364 

0 

0 930946292 


313 

0 

0.800511509 

363 

0 

0.928388747 


312 

0 

0.797953964 

362 

0 

0.925831202 


311 

0 

0.795396419 

361 

0 

0.923273657 


310 

0 

0 792838875 

360 

0 

0.920716113 


309 

0 

0.79028133 

359 

0 

0.918158568 


308 

0 

0.787723785 

358 

0 

0.915601023 


307 

0 

0.78516624 

357 

0 

0.913043478 


306 

0 

0.782608696 

356 

0 

0.910485934 


305 

0 

0.780051151 

355 

0 

0.907928389 


304 

0 

0.777493606 

354 

0 

0.905370844 


303 

0 

0.774936061 

353 

0 

0.902813299 


302 

0 

0.772378517 

352 

c 

0.900255754 


301 

0 

0 769820972 

351 

G 

0.89769821 


300 

0 

0.767263427 

350 

c 

0.895140665 


299 

0 

0.764705882 

349 

c 

0.89258312 


298 

0 

0.762148338 

348 

c 

0.890025575 


297 

c 

0.759590793 


appendix h 


Equations for the Water Clarity Criteria Biological Reference Curves 




















MESOH ALIN E/POLYHALINE 


rank 

volume 

time 

296 

0 

0.757033248 

295 

0 

0.754475703 

294 

0 

0.751918159 

293 

0 

0.749360614 

292 

0 

0.746803069 

291 

0 

0.744245524 

290 

0 

0.74168798 

289 

0 

0.739130435 

288 

0 

0.73657289 

287 

0 

0.734015345 

286 

0 

0.731457801 

285 

0 

0.728900256 

284 

0 

0.726342711 

283 

0 

0.723785166 

282 

0 

0.721227621 

281 

0 

0.718670077 

280 

0 

0.716112532 

279 

0 

0.713554987 

278 

0 

0.710997442 

277 

0 

0.708439898 

276 

0 

0.705882353 

275 

0 

0.703324808 

274 

0 

0.700767263 

273 

0 

0.698209719 

272 

0 

0.695652174 

271 

0 

0.693094629 

270 

0 

0.690537084 

269 

0 

0.68797954 

268 

0 

0.685421995 

267 

0 

0.68286445 

266 

0 

0.680306905 

265 

0 

0.677749361 

264 

0 

0.675191816 

263 

0 

0.672634271 

262 

0 

0 670076726 

261 

0 

0.667519182 

260 

0 

0.664961637 

259 

0 

0.662404092 

258 

0 

0.659846547 

257 

0 

0657289003 

256 

0 

0.654731458 

255 

0 

0.652173913 

254 

0 

0.649616368 

253 

0 

0.647058824 

252 

0 

0.644501279 

251 

0 

0.641943734 

250 

0 

0.639386189 

249 

0 

0.636828645 

248 

0 

0.6342711 

247 

0 

0.631713555 

246 

0 

0.62915601 


rank 

volume 

time 

245 

0 

0.626598465 

244 

0 

0.624040921 

243 

0 

0 621483376 

242 

0 

0.618925831 

241 

0 

0.616368286 

240 

0 

0.613810742 

239 

0 

0.611253197 

238 

0 

0 608695652 

237 

0 

0.606138107 

236 

0 

0.603580563 

235 

0 

0.601023018 

234 

0 

0.598465473 

233 

0 

0.595907928 

232 

0 

0.593350384 

231 

0 

0.590792839 

230 

0 

0.588235294 

229 

0 

0.585677749 

228 

0 

0.583120205 

227 

0 

0.58056266 

226 

0 

0.578005115 

225 

0 

0.57544757 

224 

0 

0 572890026 

223 

0 

0.570332481 

222 

0 

0.567774936 

221 

0 

0.565217391 

220 

0 

0.562659847 

219 

0 

0.560102302 

218 

0 

0.557544757 

217 

0 

0.554987212 

216 

0 

0.552429668 

215 

0 

0.549872123 

214 

0.0037 

0.547314578 

213 

0.0037 

0.544757033 

212 

0.0037 

0 542199488 

211 

0.0054 

0 539641944 

210 

0.0054 

0 537084399 

209 

0.0073 

0.534526854 

208 

0.0094 

0.531969309 

207 

0.011 

0.529411765 

206 

0.0147 

0.52685422 

205 

0.0147 

0.524296675 

204 

0.0154 

0.52173913 

203 

0.0154 

0 519181586 

202 

0.0162 

0.516624041 

201 

0.0162 

0.514066496 

200 

0.0162 

0.511508951 

199 

0.0162 

0.508951407 

198 

0.0162 

0.506393862 

197 

0.0162 

0.503836317 

196 

0.0162 

0 501278772 

195 

0.0204 

0.498721228 


appendix h • Equations for the Water Clarity Criteria Biological Reference Curves 











H-8 


MESOHAUNE/POLYH ALINE 


rank 

volume 

time 


rank 

volume 

time 

194 

0.0204 

0.496163683 


143 

0.1224 

0.3657289 

193 

0.022 

0.493606138 


142 

0.1224 

0.363171355 

192 

0.022 

0.491048593 


141 

0.1224 

0.360613811 

191 

0.0235 

0.488491049 


140 

0.1224 

0.358056266 

190 

0.0256 

0.485933504 


139 

0.1231 

0.355498721 

189 

0.0256 

0.483375959 


138 

0.1231 

0.352941176 

188 

0.027 

0 480818414 


137 

0.1231 

0.350383632 

187 

0.027 

0.47826087 


136 

0.1231 

0.347826087 

186 

0.027 

0.475703325 


135 

0.1282 

0.345268542 

185 

0.0282 

0.47314578 


134 

0.1297 

0.342710997 

184 

0.0378 

0.470588235 


133 

0.1319 

0.340153453 

183 

0.0432 

0.468030691 


132 

0.1351 

0.337595908 

182 

0.0476 

0.465473146 


131 

0.1385 

0.335038363 

181 

0.0476 

0.462915601 


130 

0.1429 

0.332480818 

180 

0.0513 

0.460358056 


129 

0.1502 

0.329923274 

179 

0.0513 

0.457800512 


128 

0.1538 

0.327365729 

178 

0.0513 

0.455242967 


127 

0.1538 

0.324808184 

177 

0.0612 

0.452685422 


126 

0.1575 

0.322250639 

176 

0.0615 

0.450127877 


125 

0.1612 

0.319693095 

175 

0.0615 

0 447570332 


124 

0.1612 

0.31713555 

174 

0.0615 

0.445012788 


123 

0.1633 

0.314578005 

173 

0.0615 

0.442455243 


122 

0.1633 

0.31202046 

172 

0.0615 

0 439897698 


121 

0.169 

0.309462916 

171 

0.0615 

0.437340153 


120 

0.1795 

0.306905371 

170 

0.0623 

0.434782609 


119 

0.1837 

0.304347826 

169 

0.0659 

0.432225064 


118 

0.1868 

0.301790281 

168 

0.0659 

0.429667519 


117 

0.1995 

0.299232737 

167 

0.0696 

0.427109974 


116 

0.2 

0.296675192 

166 

0.0703 

0.42455243 


115 

0.2041 

0.294117647 

165 

0.0728 

0.421994885 


114 

0.2088 

0.291560102 

164 

0.0769 

0 41943734 


113 

0.2108 

0.289002558 

163 

0.0806 

0.416879795 


112 

0.2113 

0.286445013 

162 

0.0811 

0.414322251 


111 

0.2254 

0.283887468 

161 

0.0939 

0 411764706 


110 

0.2308 

0.281329923 

160 

0.102 

0.409207161 


109 

0.2308 

0 278772379 

159 

0.102 

0.406649616 


108 

0.2308 

0.276214834 

158 

0.1077 

0.404092072 


107 

0.2308 

0.273657289 

157 

0.1077 

0 401534527 


106 

0.2378 

0.271099744 

156 

0.1081 

0 398976982 


105 

0.2394 

0.268542199 

155 

0.1127 

0.396419437 


104 

0.2432 

0.265984655 

154 

0.1136 

0.393861893 


103 

0.2449 

0.26342711 

153 

0.1209 

0.391304348 


102 

0.2449 

0.260869565 

152 

0.1221 

0 388746803 


101 

0.2486 

0.25831202 

151 

0.1224 

0 386189258 


100 

0.2541 

0.255754476 

150 

0 1224 

0.383631714 


99 

0.2564 

0.253196931 

149 

0.1224 

0.381074169 


98 

0.2582 

0.250639386 

148 

0.1224 

0.378516624 


97 

0.2653 

0.248081841 

147 

0.1224 

0.375959079 


96 

0.2653 

0 245524297 

146 

0.1224 

0.373401535 


95 

0.2811 

0.242966752 

145 

0.1224 

0.37084399 


94 

0.2857 

0.240409207 

144 

0.1224 

0 368286445 


93 

0.2857 

0 237851662 


appendix h 


Equations for the Water Clarity Criteria Biological Reference Curves 














H-9 


MESOHAUNE/POLYHALINE 


rank 

volume 

time 

41~ 

0.5838 

0.104859335 

40 

0.5897 

0.10230179 

39 

0.6 

0.099744246 

38 

0.6054 

0.097186701 

37 

0.6154 

0.094629156 

36 

0.6315 

0.092071611 

35 

0.641 

0.089514066 

34 

0.6462 

0.086956522 

33 

0.6667 

0.084398977 

32 

0.6714 

0.081841432 

31 

0.6923 

0.079283887 

30 

0.6923 

0.076726343 

29 

0.6923 

0.074168798 

28 

0.7077 

0.071611253 

27 

0.7077 

0.069053708 

26 

0.7297 

0.066496164 

25 

0 7347 

0.063938619 

24 

0.7538 

0.061381074 

23 

0.7551 

0.058823529 

22 

0.7568 

0.056265985 

21 

0.7949 

0.05370844 

20 

0.7959 

0.051150895 

19 

0.8239 

0.04859335 

18 

0.838 

0 046035806 

17 

0.8498 

0 043478261 

16 

0.8571 

0.040920716 

15 

0.8571 

0.038363171 

14 

0.8615 

0.035805627 

13 

0.9121 

0.033248082 

12 

0.9385 

0.030690537 

11 

0.9388 

0.028132992 

10 

1 

0.025575448 

9 

1 

0.023017903 

8 

1 

0.020460358 

7 

1 

0.017902813 

6 

1 

0.015345269 

5 

1 

0.012787724 

4 

1 

0.010230179 

3 

1 

0.007672634 

2 

1 

0.00511509 

1 

1 

0.002557545 


1 

0 


rank 

volume 

time 

92 

0.2857 

0.235294118 

91 

0.2973 

0.232736573 

90 

0.3081 

0.230179028 

89 

0.3187 

0.227621483 

88 

0.3216 

0.225063939 

87 

0.3239 

0.222506394 

86 

0.3243 

0.219948849 

85 

0.3265 

0.217391304 

84 

0.3405 

0.21483376 

83 

0.3451 

0.212276215 

82 

0.3469 

0.20971867 

81 

0.348 

0.207161125 

80 

0.359 

0.204603581 

79 

0.359 

0.202046036 

78 

0.359 

0.199488491 

77 

0.3592 

0.196930946 

76 

0.3622 

0.194373402 

75 

0.3692 

0.191815857 

74 

0.3838 

0.189258312 

73 

0.3846 

0.186700767 

72 

0.3892 

0.184143223 

71 

0.3919 

0.181585678 

70 

0.392 

0.179028133 

69 

0.4 

0.176470588 

68 

0.4 

0173913043 

67 

0.4 

0.171355499 

66 

0.4054 

0.168797954 

65 

0.4202 

0.166240409 

64 

0.4286 

0.163682864 

63 

0.4324 

0.16112532 

62 

0.439 

0.158567775 

61 

0.4432 

0.15601023 

60 

0 4615 

0.153452685 

59 

0.4703 

0.150895141 

58 

0.4769 

0.148337596 

57 

0.4812 

0.145780051 

56 

0.4872 

0.143222506 

55 

0.4872 

0.140664962 

54 

0.4872 

0.138107417 

53 

0.5077 

0.135549872 

52 

0.5077 

0.132992327 

51 

0.5092 

0.130434783 

50 

0.5094 

0.127877238 

49 

0.5102 

0.125319693 

48 

0.5128 

0.122762148 

47 

0.5128 

0.120204604 

46 

0.5231 

0.117647059 

45 

0.5385 

0.115089514 

44 

0.5495 

0.112531969 

43 

0.5657 

0.109974425 

42 

0.5692 

0.10741688 


appendix h • Equations for the Water Clarity Criteria Biological Reference Curves 












1-1 


appendix | 

Evaluation of Maryland and 
Virginia Chesapeake Bay 
Segment SAV Acreages from 
2003 to 2005 for Prioritizing 
Shallow-water Monitoring 
by Segment 


MARYLAND 


Chesapeake Bay 






Single Best Year 

Program 





State-adopted 

as % of SAV 

Segments/ 




2003-2005 Single 

SAV Restoration 

Restoration 

Subsegments 

2003 Acres 

2004 Acres 

2005 Acres 

Best Year Acres 

Acreage 

Acreage 


CHS0H 

0 

4 

228 

228 

77 

3 

BSH0H 

390 

1,025 

726 

1,025 

350 

3 

B0H0H 

288 

730 

918 

918 

354 

3 

CB20H 

212 

1,303 

1,071 

1,303 

705 

2 

PAXTF 

217 

220 

324 

324 

205 

2 

SAS0H 

371 

1,272 

1,476 

1,476 

1,168 

1 

CftDOH 

0 

8 

9 

9 

7 

1 

PAX0H 

106 

106 

125 

125 

115 

1 

GUN0H 

489 

2,392 

1,733 

2,392 

2,432 

1 

MATTF 

612 

601 

770 

770 

792 

1 

ELK0H 

346 

1,913 

1,964 

1,964 

2,034 

1 

PISTF 

212 

507 

757 

757 

789 

1 

POTTF(MD) 

885 

1,256 

2,029 

2,029 

2,142 

1 

N0RTF 

46 

84 

78 

84 

89 

1 

SEVMH 

222 

388 

426 

426 

455 

1 

CB1TF 

7,574 

10,110 

9,193 

10,110 

12,903 

1 

MID0H 

391 

671 

454 

671 

879 

1 

PATMH 

7 

183 

279 

279 

389 

1 


Statui 



appendix i 


Evaluation of SAV Acreages from 2003 to 2005 for Prioritizing Shallow water Monitoring 



1-2 


MARYLAND (continued) 


Cheaapeake Bay 
Program 
Sag manta/ 


2003-2005 Singla 


State-adopted 
SAV Rastoration 


Singla Bast Vaar 
aa % of SAV 
Reatoration 


Subaegmenta 

2003 Acrea 

2004 Acrea 

2005 Acrea 

Beat Year Acrea 

Acreage 

Acreage 

POTOH(MD) 

1,384 

1,408 

1,888 

1,888 

2,802 

1 

CB3MH 

23 

909 

567 

909 

1,370 

1 

HNGMH 

2,844 

3,433 

4,376 

4,376 

7,761 

1 

MAGMH 

169 

300 

308 

308 

579 

1 

CH0MH1 

2,972 

3,774 

2,293 

3,774 

8,184 

0 

POTMH(MD) 

2,430 

3,063 

2,893 

3,063 

7,088 

0 

BIGMH 

451 

550 

710 

710 

2,043 

0 

LCHMH 

784 

1,221 

260 

1,221 

4,076 

0 

EASMH 

1,639 

1,040 

768 

1,639 

6,209 

0 

CHSMH 

117 

731 

462 

731 

2,928 

0 

TANMH(MD) 

4,725 

4,554 

5,801 

5,801 

24,757 

0 

CB5MH(MD) 

700 

398 

919 

919 

8,270 

0 

WSTMH 

23 

0 

0 

23 

238 

0 

S0UMH 

14 

46 

10 

46 

479 

0 

MANMH 

235 

291 

410 

410 

4,353 

0 

FSBMH 

15 

17 

7 

17 

197 

0 

POCMH(MD) 

58 

69 

69 

69 

877 

0 

PAXMH 

37 

42 

0 

42 

1,634 

0 

CB4MH 

21 

10 

0 

21 

2,533 

0 

CH0MH2 

0 

1 

0 

0 

1,621 

0 

CHOOH 

0 

0 

0 

0 

72 

0 

NANMH 

0 

0 

0 

0 

3 

0 

NANOH 

0 

0 

0 

0 

12 

0 

RHDMH 

0 

0 

0 

0 

60 

0 

WICMH 

0 

0 

0 

0 

3 

0 

BACOH 

0 

30 

0 

30 


N/A 

CHOTF 

0 

0 

0 

0 



CHSTF 

0 

0 

1 

1 



NANTF 

0 

0 

0 

0 



POCOH 

0 

0 

0 

0 



POCTF 

0 

0 

0 

0 



WBRTF 

0 

0 

0 

0 




Statua 


No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
NO SAV 


appendix i • Evaluation of SAV Acreages from 2003 to 2005 for Prioritizing Shallow-water Monitoring 





1-3 


VIRGINIA 


Cheiapeaks Bay 
Program 


State-adopted 


Single Best Year 
as % of SAV 


Segments/ 

Subsegments 

2003 Acres 

2004 Acres 

2005 Acres 

2003-2005 Single 
Best Year Acres 

SAV Restoration 
Acreage 

Restoration 

Acreage 

MPNTF 

184 

179 

296 

296 

85 

3 

PMKTF 

217 

334 

585 

585 

187 

3 

POTOH(VA) 

1,950 

2,326 

2,627 

2,627 

1,503 

2 

CHKOH 

425 

432 

697 

697 

535 

1 

RPPTF 

0 

24 

81 

81 

66 

1 

POTTF(VA) 

761 

1,197 

2,336 

2,336 

2,093 

1 

CB8PH 

5 

6 

9 

9 

11 

1 

CB7PH 

9,192 

7,157 

8,139 

9,192 

15,107 

1 

JMSOH 

9 

0 

0 

9 

15 

1 

CB6PH 

707 

488 

642 

707 

1,267 

1 

MOBPH 

8,457 

7,549 

7,205 

8,457 

15,901 

1 

JMSPH 

132 

74 

0 

132 

300 

0 

POCMH(VA) 

1,608 

1,094 

1,716 

1,716 

4,066 

0 

CRRMH 

43 

224 

292 

292 

768 

0 

TANMH(VA) 

4,682 

3,990 

5,036 

5,036 

13,579 

0 

CB5MH(VA) 

* 

1,833 

2,464 

2,464 

7,633 

0 

YRKPH 

887 

597 

438 

887 

2,793 

0 

LYNPH 

0 

9 

19 

19 

107 

0 

PIAMH 

447 

443 

561 

561 

3,479 

0 

RPPMH 

21 

33 

198 

198 

1,700 

0 

POTMH(VA) 

55 

339 

444 

444 

4,250 

0 

JMSTF 

75 

12 

53 

75 

1,200 

0 

JMSMH 

2 

2 

0 

2 

200 

0 

APPTF 

0 

0 

0 

0 

379 

0 

YRKMH 

0 

0 

0 

0 

239 

0 

EBEMH 

0 

0 

0 

0 

- 


ELIMH 

0 

0 

0 

0 

- 


ELIPH 

0 

0 

0 

0 

- 


LAFMH 

0 

0 

0 

0 

- 


MPNOH 

0 

0 

0 

0 

- 


PMKOH 

0 

0 

0 

0 

- 


RPPOH 

0 

0 

4 

4 

- 


SBEMH 

0 

0 

0 

0 

- 


WBEMH 

0 

0 

0 

0 

- 

- 


Status 



No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
No SAV 
No SAV 


appendix i 


Evaluation of SAV Acreages from 2003 to 2005 for Prioritizing Shallow water Monitoring 





1-4 





DELAWARE 




Chesapeake Bay 
Program 

Segments/ 

Subsegments 

2003 Acres 

2004 Acres 

2003-2005 Single 
2005 Acres Best Year Acres 

State-edopted 
SAV Restoration 
Acreage 

Single Best Year 
as % of SAV 
Restoration 
Acreage 

Status 


NANTF(DE) 


DISTRICT OF COLUMBIA 


POTTF(DC) 
ANATF (DC) 


; PASS (>100% of Goal) 
FAIL (50%-< 100% of Goal) 
FAIL (<50% of Goal) 

No SAV Goal 


’Partial data available that year 


appendix i 


Evaluation of SAV Acreages from 2003 to 2005 for Prioritizing Shallow-water Monitoring 




J-1 


appendix 



Chesapeake Bay Estuarine 
Benthic Communities 
Assessment Protocol for 
Maryland and Virginia 
305b/303d Integrated Reports 


Maryland (Department of the Environment, Department of Natural Resources), 
Virginia (Department of Environmental Quality) and U.S. EPA (Region 3 Water 
Protection Division and Chesapeake Bay Program Office) reached agreement on the 
protocol to assess Chesapeake Bay benthic community health. This appendix docu¬ 
mented the assessment protocol supporting the States evaluation of Chesapeake Bay 
benthic community data as part of their 305b/303d Integrated Reports. This assess¬ 
ment protocol builds directly on the more detailed assessment methods 
recommended by Llanso et al. 2005 (see Appendix K). 

The overall decision protocol is shown in Figure J-1. Phase I consists of the evalu¬ 
ation of the sample size ( i.e ., number of B-IBI scores) available from the waterbody 
segment during the five-year assessment window. If the sample size satisfies the 
requirements of the statistical method (N > 10), a formal assessment of status (i.e. 
impaired vs. supports aquatic life use) is determined utilizing the “percent degraded 
area” statistical methodology (Phase II). If the sample size requirement is not met 
an impairment assessment based solely on these analyses is not possible. Results for 
segments with insufficient sample size should still be examined for possible use in 
conjunction with other assessment data of the 305b/303d reporting process. 

Phase II consists of the impairment assessment of aquatic life use attainment based 
on a comparison of Benthic Index of Biotic Integrity (B-IBI) scores and can only be 
performed when the number of B-IBI scores within a specified waterbody segment 
is sufficient to meet the sample size requirement of the approved statistical method 
(N > 10). Phase II can result in one of two possible outcomes: (1) the segment is not 
impaired for Aquatic Life use due to benthic community status (note that the 
segment may still be impaired for aquatic life use due to failure of other aquatic life 
use criteria), or (2) the segment fails to support aquatic life use due to benthic 
community status and is assessed as impaired. Best professional judgment can be 


appendix J 


Bay Estuarine Benthic Communities Assessment Protocol for Maryland and Virginia 


J-2 


Phase 1 


Phase II 


Phase III 


Sample Size 
Evaluation 


Impairment Assessment 


Segment Characterization 





(Identify Probable Causes) 






N < 10 

Yes — 

Insufficient sample size 

— 

Optional use of 

B-IBI scores and diagnostic analyses 
in conjunction with other available 
data for305b/303d Integrated Report 


] No 




N> 10 

Yet - 

Apply Degraded Area 
Statistical method 




i 




Segment declared 
'nut impaired* for bcnlbic 
aquatic life communities 
in 305b/303d Integrated 
Report 

Yes — 

Optional ase of 

B-IBI scores and diagnostic analyses 
in conjunction with other available 
data for305b/303d Integrated Report 



1 No 




Segment declared 
'impaired* for benthic 
aquatic life communities in 
30$b/303d Integrated 
Report 

Ye* —» 

Apply diagnostic analyses for 
assignment of suspected causefs) of 
degradation in 305b/3fl3d Integrated 
Report 







Figure J-1. Overall Chesapeake Bay benthic index of biotic integrity assessment decision 
protocol. 


applied to override (reverse) the outcome of the formal statistical analysis results, but 
such reversals must be justified and documented. 

Phase III consists of the identification of probable causes of benthic impairment of the 
waterbody segment based upon benthic stressor diagnostic analyses. It is a two-step 
procedure that involves (1) Site Classification, and (2) Segment Characterization. 

1. Site classification: The first step is to assign probable cause of benthic degra¬ 
dation to each individual “degraded” benthic sample. For purposed of these 
diagnostic analyses, a sample is considered degraded if the B-IBI score is less 
than 2.7. 

Site Classification—Step la: The application of a formal statistical linear 
discriminant function calculates the ‘inclusion probability’ of each degraded 
site belonging to a ‘contaminant caused’ group or an ‘other causes’ group, 
based upon its B-IBI score and associated metrics. If a site is assigned to the 
‘Contaminant’ Group with a probability > 0.9, this site is considered impacted 
by contaminated sediment and no further classification is required. 

Site Classification—Step lb: If a site is classified as degraded due to ‘other 
causes (i.e., not contaminant-related), an evaluation of the relative abundance 
(and/or biomass) of the benthos is examined. Scores for both abundance and 
biomass are considered to be bipolar for the Chesapeake Bay Benthic IBI. For 
either metric; a high score of 5, indicating desirable conditions, falls in the mid¬ 
range of the abundance/biomass distributions, while a low score of 1, 
indicating undesirable conditions, can result either from insufficient abun- 

appendix j • Bay Estuarine Benthic Communities Assessment Protocol for Maryland and Virginia 


























J-3 


dance/biomass or excessive abundance/biomass. The scoring thresholds for 
these two metrics vary with habitat type (salinity regime and substrate type) as 
summarized in Figure J-2. In this process, a site is classified as degraded by 
low dissolved oxygen” if the abundance (and/or biomass) metric scores a 1 
due to insufficient abundance (and/or biomass). Alternatively, if the abundance 
(and/or biomass) metric scores a 1 because of excessive abundance (and/or 
biomass) the site is classified as degraded by “eutrophication”. 

2. Segment classification: The assignment of probable causes of benthic degra¬ 
dation for the overall segment is accomplished using a simple 25% rule. If the 
percent of total sites in a segment impacted by a single cause (i.e. sediment 
contaminants, low dissolved oxygen, or eutrophication) exceeds 25%, then that 
cause is assigned. If no causes exceed 25%, the cause is considered unknown. 
The cause(s) should be identified as a suspected (vs. verified) cause of benthic 
community degradation in the ADB database. 

Table J-l shows the possible conclusions from applying the above protocol. The 
States should carefully review the results from application of the protocol to ensure 
all findings and conclusions are rational and reasonable. Best profession judgment, 
common sense, and ancillary information about each segment should be utilized as 
necessary and available. 


Habitat 

Metric 

Lower Limit 
(Metric 
Score=1) 

Upper Limit 
(Metric 
Score=1) 


Tidal Freshwater 

Abundance (# m-2) 
Biomass (q m-2) 

<800 

a 5500 


Oligohaline 

Abundance (U m-2) 
Biomass (g m-2) 

<180 

2 4050 

Low Mesohaline 

Abundance (# m-2) 
Biomass (g m-2) 

<500 

<1 

2 6000 

2 30 


High Mesohaline Sand 

Abundance (# m-2) 
Biomass (g m-2) 

<1000 

<1 

5 5000 

2 50 


High Mesohaline Mud 

Abundance (# m-2) 
Biomass (g m-2) 

<1000 

<0.5 

a 5000 
> 50 


Polyhaline Sand 

Abundance (# m-2) 
Biomass (g m-2) 

<1500 

<1 

2 8000 

2 50 


Polyhaline Mud 

Abundance (# m-2) 
Biomass (g m-2) 

<1000 

<0.5 

2 8000 

2 30 


Figure J-2. Metric scoring for eutrophication and low dissolved oxygen causes. 
Source: Llanso 2002, Table 9, pages 24-26. 


appendix j • Bay Estuarine Benthic Communities Assessment Protocol tor Maryland and Virginia 


















J-4 


Table J-1. Possible conclusions from application of the assessment protocol. 


n>=10 - sufficient sample size for assessment 



Impairment Analysis 

Stressor Diagnostic Analyses 

Scenario 

CL-L 

(P-P.) 

(Table 3 of 
VERSAR 
Technical 
Report) 

Impaired. 
Degraded Area 
method? 
(Table 3 of 
VERSAR 
Technical 
Report) 

Samples with 
contaminant 
Posterior Prob. 
p>= 0.90; % of 
Total (Table 5 of 
VERSAR Technical 
Report) 

Degraded Samples with 
excessive Abundance/Biomass; 

% of Total w/o Cont. (Table 5 of 
VERSAR Technical Report) 

Degraded Samples with 
Insufficient 

Abundance/Biomass; % of 
Total w/o Cont. (Table 5 of 
VERSAR Technical Report) 

1 

£0 

No 

review as 
supplemental Info 

review as supplemental info 

review as supplemental info 


A small, non-significant fraction of IBI scores are within or below the lower range of the reference distribution so water quality conditions in this 
segment support the benthic community (no Impairment) 

\Miere community samples are degraded, the stressor analyses may provide Information that supports other assessment data 


2 

>0 

Yes 

£ 25% of Total 

£ 25% of Total Samples 

£ 25% of Total Samples 




Samples 




A large significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses do not suggest dominant stressors affecting community composition Cause of degradation is “unknown" 


3 

>0 

Yes 

> 25% of Total 

£ 25% of Total Samples 

£ 25% of Total Samples 




Samples 




A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses suggest sediment contaminants as a likely pollutant affecting benthic community structure 


4 

>0 

Yes 

> 25% of Total 
Samples 

> 25% of Total Samples 

£ 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses suggest sediment contaminants as a likely pollutant affecting benthic community structure Observation of high 
biomass or abundance is indicative of eutrophic conditions as an additional stressor affecting the benthic community 


5 

>0 

Yes 

> 25% of Total 
Samples 

£ 25% of Total Samples 

> 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses suggest sediment contaminants as a likely pollutant affecting benthic community structure Samples observed with 
low biomass or abundance are indicative of low dissolved oxygen as an additional stressor affecting the benthic community 


6 

>0 

Yes 

£ 25% of Total 
Samples 

> 25% of Total Samples 

£ 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses do not suggest sediment contaminants as a stressors affecting community composition Samples observed with 


7 

>0 

Yes 

£ 25% of Total 
Samples 

> 25% of Total Samples 

> 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses do not suggest sediment contaminants as stressor affecting community composition Samples observed with high 
biomass or abundance are indicative of eutrophic conditions within the segment while other samples observed with low biomass or abundance 
are indicative of low dissolved oxygen as another stressor within the segment 


8 

>0 

Yes 

£ 25% of Total 
Samples 

£ 25% of Total Samples 

> 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses do not suggest sediment contaminants as a stressor affecting community composition Samples observed with 


9 

>0 

Yes 

> 25% of Total 

Samples 

> 25% of Total Samples 

> 25% of Total Samples 


A large, significant fraction of IBI scores are within or below the lower range of the reference distribution, so water quality conditions in this 
segment do not support the benthic community (impaired condition) 

Stressor diagnostic analyses suggest sediment contaminants as a likely pollutant affecting benthic community structure Samples observed with 
high biomass or abundance are indicative of eutrophic conditions within the segment while other samples observed with low biomass or 
abundance are indicative of low dissolved oxygen as an additional stressor within the segment 


appendix j 


Bay Estuarine Benthic Communities Assessment Protocol for Maryland and Virginia 
























































































J-5 


Table J-1. (continued) 


n<10 - small sample size, insufficient for analysis 



Impairment Analysis 

Stressor Diagnostic Analyses 

Scenario 

CL-L 

(P-Po) 
(Table 3 of 
VERSAR 
Technical 
Report) 

Impaired: 
Degraded 
Area? (Table 3 
of VERSAR 
Technical 
Report) 

Samples with 
contaminant 
Posterior Prob. 
p>= 0.90; % of 
Total (Table 5 of 
VERSAR 
Technical 
Report) 

Degraded Samples with 
excessive 

Abundance/Biomass; % of 
Total w/o Cont. (Table 5 of 
VERSAR Technical Report) 

Degraded Samples with Insufficient 
Abundance/Biomass; % of Total w/o 
Cont. (Table 5 of VERSAR Technical 
Report) 

1 

n/a 

Unknown, Not 
Assessed 

review as 
supplemental info 

review as supplemental info 

review as supplemental info 


• There are too few samples to define the confidence interval of benthic sample IBIs, so in this segment - the biological community condition is 
unknown 


• Where community samples are identified as degraded, information from the stressor diagnostic analyses may provide supplemental information 
that may support other assessment data 


LITERATURE CITED 


Llanso, R.J. 2002. Methods for Calculating the Chesapeake Bay Benthic Index of biotic 
Integrity. Versar Inc., Columbia, Maryland http://www.baybenthos.versar.com/docs/Ches- 
BayBIBI.PDF 

Llanso , R.J., J.H. Volstad, D.M. Dauer, and M.F. Lane. 2005. 2006 303(D) Assessment 
Methods For Chesapeake Bay Benthos. Final Report Submitted to Virginia Department of 
Environmental Quality, Richmond, Virginia. September 2005. 


appendix i 


Evaluation of SAV Acreages from 2003 to 2005 for Prioritizing Shallow-water Monitoring 























































































































K-1 


appendix 



2006 303(d) Assessment 
Methods for Chesapeake Bay 

Benthos 


Final Report Submitted to: 

Virginia Department of Environmental Quality 
629 East Main Street 
Richmond, Virginia 23230 


Submitted by: 

Roberto J. Llanso 
Jon H. V0lstad 

Versar, Inc., Columbia, Maryland 


Daniel M. Dauer 
Michael F. Lane 

Department of Biological Sciences 
Old Dominion University 
Norfolk, Virginia 

September 2005 


FOREWORD 

This report, 2006 303(d) Assessment Methods for Chesapeake Bay Benthos, was 
prepared by Versar at the request of the Virginia Department of Environmental 
Quality, under Purchase Order # 11646 between Versar, Inc. and the Commonwealth 
of Virginia. Old Dominion University contributed to the diagnostic (discriminant 
tool) assessment and to project conceptualization and evaluation. The statistical 
analyses for the 2006 impairment assessment were conducted by Dr. Ed Weber and 
Ms. Jody Dew, of Versar. Dr. Weber also contributed to the development of the 
Degraded Area method presented in this report. 


appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



K-2 


1.0 INTRODUCTION 

To meet the requirements of the Clean Water Act, the States of Maryland and 
Virginia are using benthic biological criteria for reporting overall condition and iden¬ 
tification of impaired waters in Chesapeake Bay. The Chesapeake Bay benthic index 
of biotic integrity (B-IBI) is the basis for these biological criteria. Previous work 
conducted by Versar and Old Dominion University had two objectives: to develop a 
methodology for the assessment of benthic community status for 303(d) impairment 
decisions and to produce an assessment for each of the Chesapeake Bay segments 
and sub-segments containing benthic community data. A statistical procedure was 
developed that tests whether the distribution of B-IBI scores from probability-based 
samples collected from a Bay segment is significantly different from the distribution 
of scores from reference sites (Llanso et al. 2003). This procedure, a stratified 
Wilcoxon rank sum test, was evaluated and applied to the 2004 assessment data. The 
assessment resulted in 26 segments considered impaired based upon benthic 
community condition. The Wilcoxon approach, however, was sensitive to small 
shifts in B-IBI scores relative to the reference condition, even in some cases where 
a majority of the B-IBI scores in a segment met the restoration goals. For stratified 
data (i.e., the habitat types of the B-IBI, see below) it was not possible to estimate 
the magnitude of the shift, for example by using a Hodges-Lehman confidence 
interval. Thus, with the Wilcoxon approach we were unable to estimate the magni¬ 
tude of degradation: the difference between the segment and the reference condition. 
A small difference could be statistically significant but of little ecological relevance. 
It was recommended that alternative methods be evaluated, especially those that take 
into account magnitude of departure from reference conditions and whether this 
magnitude is above specific thresholds of protection that the States may wish to 
implement. For the 2006 303(d) report, we developed a new method that quantifies 
magnitude of degradation. We call this method “Degraded Area.” In the present 
report, we describe the Degraded Area method, apply this method and the Wilcoxon 
approach to the 2006 assessment data, and compare the results. 

In addition, a benthic diagnostic tool has been developed that can be used to identify 
potential sources of stress affecting benthic community condition in the Chesapeake 
Bay (Dauer et al. 2002). The tool can distinguish stress due to contaminants versus 
stress due to other factors (e.g., low dissolved oxygen, or unknown). This screening 
tool was used to identify which impaired segments have a high probability of sedi¬ 
ment contamination. These segments could then be targeted for additional sampling 
or evaluation. The B-IBI metric scores for abundance and biomass were also used to 
identify (1) insufficient abundance patterns consistent with a low dissolved oxygen 
effect and (2) excessive abundance patterns consistent with eutrophication effects. 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



K-3 


2.0 OBJECTIVES 

1. Develop a new method for the assessment of Chesapeake Bay benthic commu¬ 
nity status for 303(d) impairment decisions. 

2. Produce an assessment for the 2006 303(d) report using both the new method 
and the Wilcoxon approach. 

3. Apply the benthic diagnostic tool and the insufficient/excessive abundance 
criteria to the 2006 assessment data. 


3.1. DATA 


3.0 METHODS 


Like the Wilcoxon (described in Llanso et al. 2003), the Degraded Area method 
compares reference data sets to assessment data sets. The reference data set 
consisted of the calibration and validation data used to develop the Chesapeake Bay 
benthic index of biotic integrity (B-IBI). The Chesapeake Bay B-IBI is described in 
Weisberg et al. (1997) and Alden et al. (2002). The B-IBI consists of benthic 
community metrics and scoring thresholds (metric values) that were developed sepa¬ 
rately for seven habitat types (Table 1). The numbers of reference samples in each 
habitat used to develop the B-IBI, the Wilcoxon approach, and the method described 
in this report are listed in Table 2. The reference samples were either “good” (=unde- 
graded, collected at sites known to have good sediment and water quality) or 
“degraded” (collected at sites with low dissolved oxygen, organic enrichment, or 
high sediment contaminant concentrations and toxicity). To develop the B-IBI, Weis¬ 
berg et al. (1997) used averages of three replicate samples per site for mesohaline 
and polyhaline habitats, while Alden et al. (2002) used single replicate samples for 
tidal fresh and oligohaline habitats. We used the same metrics values produced by 
these two studies, but re-calculated B-IBI scores from these metrics to be consistent 
with the latest B-IBI methodology. The methods for the calculation of the Chesa¬ 
peake B-IBI are described in the World Wide Web at: http://www.baybenthos. 
versar.com/ referenc.htm. 

The assessment data for the 2006 303(d) report consisted of random samples 
collected from 2000 to 2004 throughout the Chesapeake Bay. A total of 1,430 
samples (single replicates) were used, including 750 samples collected by the Mary¬ 
land Chesapeake Bay benthic monitoring program, 500 samples collected by the 
Virginia Chesapeake Bay benthic monitoring program, 150 samples collected by the 
Elizabeth River benthic biological monitoring program, and 10 samples collected for 
a gear comparison study in each of Mobjack Bay, the tidal fresh Mattaponi River, 
and the Nansemond River. All assessment samples were collected with a Young grab 
(440 cm 2 surface area, 0.5-mm screen). For sample collection methods, see the 
benthic monitoring program comprehensive reports posted at the World Wide Web 
address given above. 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 




K-4 


Assessments were produced for each of 85 Chesapeake Bay Program segments and 
sub-segments containing benthic data. Segments (TMWA 1999) are Chesapeake Bay 
regions having similar salinity and hydrographic characteristics. In Virginia, 
segments were sub-divided into smaller units by the Virginia Department of Envi¬ 
ronmental Quality. Sub-segments were produced for each of the mainstems of rivers 
and bays (e.g., James River mesohaline) and for some of the smaller systems 
opening into the mainstem (e.g.. Pagan River). Assessment samples were assigned to 
segments and sub-segments using GIS software. Hydrographic data collected synop- 
tically with the benthic data were used to assign each sample to one of seven habitat 
classes used in the calculation of the B-IBI. These are the same habitat classes used 
in the reference data set. 

3.2. DEGRADED AREA 

The new method developed for the 2006 assessment was based on the confidence 
limit and bootstrap simulation concepts described in Alden et al. (2002). Specifi¬ 
cally, bootstrap simulation (Efron and Tibshirani 1998) was applied to incorporate 
uncertainty in reference conditions. Bootstrap simulation is used to assess the accu¬ 
racy of an estimate by randomly sampling n times, with replacement, from an 
original data set. In our case, we wished to estimate the score corresponding to the 
5 th percentile of the B-IBI reference distributions for the good sites (by habitat). 
Because the reference distributions were based on small sample sizes, the percentiles 
were not well defined and would likely vary if different sets of reference sites were 
sampled. Thus the need to estimate this parameter more accurately with bootstrap 
simulations. Bootstrap simulations make no assumptions, except that the reference 
data are a representative sample from a “super population” of reference sites. 

For each habitat, a threshold based on the 5th percentile B-IBI score of the reference 
data set for the good sites (or the maximum B-IBI score observed for the degraded 
sites, see below), was determined. This threshold was not intended to serve as a crite¬ 
rion for classifying individual B-IBI scores, rather it was used to categorize the 
segment as impaired or not based on the proportion of sites below the threshold (i.e., 
degraded area) and the variance associated with this estimate. The variance in the 
estimates of proportions for each segment was estimated by the simulations. 

The B-IBI scores for the reference good and degraded sites had degrees of overlap 
that ranged from quite high in the tidal freshwater and oligohaline habitats to moder¬ 
ately low in the mesohaline and polyhaline habitats. An assessment sample is more 
likely to come from an impaired benthic community if the B-IBI score for this 
sample is within the range of scores observed for sites known to be degraded. There¬ 
fore, two criteria were established for determining the threshold: its score had to be 
within the lower bound of the good reference distribution (i.e., 5th percentile), and 
it had to be within the upper range of observed scores for known degraded sites (i.e., 
the reference degraded sites). If the 5th percentile score for a simulation run was not 
within the range of scores for the reference degraded sites, then the maximum B-IBI 
score for the reference degraded sites was selected as the threshold. Thus, in this 
study, sites with low B-IBI scores below thresholds were likely to be impaired and 
unlikely to come from good reference areas. 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-5 


In each simulation run, a subset of the reference good sites for each habitat was 
selected at random, and the B-IBI threshold for this subset was determined (i.e., the 
IBI score at the 5th percentile, or the maximum score for the reference degraded 
samples). The scores of the assessment data for each habitat were then compared to 
the threshold to estimate the proportion of sites below the threshold. By repeating 
this process over and over again (5,000 runs) we were able to estimate the variance 
in the proportion of sites below the threshold from the bootstrap estimates. This vari¬ 
ance reflects variability in the thresholds as well as sampling variability in the 
assessment data. 

In the final step of the method, segments were declared impaired if the proportion of 
sites below the threshold (i.e., degraded area) was significantly higher than expected 
under the null hypothesis. Under the null hypothesis, a small number of sites 
(defined as 5% of the sites) would be expected to have low IBI scores even if all sites 
in a segment were in good condition (i.e., no low dissolved oxygen, contaminant, or 
nutrient enrichment problems). This is because of natural variability in the benthic 
communities, the effects of natural stressors, and sampling and methodological error. 
For a segment to be declared as impaired, the lower bound of the 95% confidence 
interval of the estimate had to be higher than 5% (the expected proportion under the 
null hypothesis), with a minimum sample size of 10. A 5% level was used in agree¬ 
ment with standard statistical practice. 

The steps described above are summarized below and in Appendix A: 

1. Thresholds are set for each of seven benthic habitats in Chesapeake Bay. 

2. The threshold is set as the smaller of two values: 5th percentile IBI score for 
the good reference sites or maximum observed IBI score for the degraded refer¬ 
ence sites. 

3. The 5th percentile score and its variance is estimated by bootstrap simulations. 

4. For each iteration of the bootstrap simulation, a subset (of same sample size) 
of the good reference sites for each habitat is selected at random (with replace¬ 
ment), and the 5th percentile score determined. 

5. At each iteration, the threshold is set according to #2. 

6. At each iteration, the assessment data are compared to the reference data to 
estimate the proportion of sites (P) with scores below the threshold. This is 
done for each of one or more habitats within a segment. 

7. P is averaged over all the iterations. 

8. Under the null hypothesis, 5% of the sites (Po) would be expected to have low 
IBI scores, even if all sites in a segment were in good condition. 

9. Segments are declared impaired if P — Po > 0 (greater than expected under the 
null hypothesis, with 95% confidence) (See Schenker and Gentleman 2001). 

3.3. WILCOXON 

A stratified Wilcoxon rank sum test was applied as described in Llanso et al. (2003) 
using Proc-StatXact 5 software (Cytel Software Corporation 2002). B-IBI scores 
were grouped into three ordered condition categories (1.0-2.0, 2.1-2.9, 3.0-5.0) and 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-6 


the distribution of scores in each category within a segment was compared for each 
habitat to the distribution of scores for the good reference condition. Under the null 
hypothesis (Ho) of no impairment, the two populations (segment and reference) 
were considered to have the same underlying multinomial distributions of samples 
among the ordered categories. The assessment of impairment was based on a one¬ 
sided exact test of Ho against the alternative hypothesis that the segment had a 
distribution shifted towards lower B-IBI scores than for the reference condition. The 
ranking was done separately by habitat, and then combined across habitats. 
Segments with a minimum of 10 samples for which the test was significant at the 1 % 
alpha level and 90% power, were considered impaired under this method. 

3.4. BENTHIC DIAGNOSTIC TOOL 

The benthic diagnostic tool allows environmental managers to identify potential 
sources of anthropogenic stress to benthic communities within Chesapeake Bay. The 
development and application of the tool was described in detail in Dauer et al. (2002, 
2005). The benthic diagnostic tool is based on a linear discriminant function that 
classifies sites in Chesapeake Bay identified as having degraded benthic communi¬ 
ties into categories distinguished by the type of stress experienced by those 
communities. Presently, the function is capable of discriminating contaminated sites 
from sites affected by all other potential sources of stress in any of the seven benthic 
habitat types of Chesapeake Bay. Sites are classified into two groups: 1) a contami¬ 
nant group and 2) the other group representing all other potential sources of stress 
(eutrophication, low dissolved oxygen, etc.). This function is a linear combination of 
variables that includes over 60 measures of diversity, dominance, and function of 
benthic communities. The score for the function is used to calculate the probabilities 
that a sample is drawn from both groups and the sample is assigned to the group to 
which it has the highest probability of belonging. These probabilities are typically 
referred to as posterior probabilities of group membership. 

For this assessment, sites with B-IBI scores < 2.7 were defined as “degraded” for 
benthic diagnostic tool application purposes. A score of 2.7 is used in the Chesa¬ 
peake Bay benthic monitoring programs to define benthic community degradation. 
This cutoff value may differ from the threshold used by the Degraded Area method 
to determine proportion of sites with degraded benthic communities, but it should be 
very close to that threshold. Because cutoff values differ, diagnostic tool percentages 
should only be used as a general guide for identifying potential causes of degrada¬ 
tion. For each “degraded” site, benthic metric values were submitted to the function 
and posterior probabilities of group membership calculated. Posterior probabilities 
for impaired segments were then used to identify the most likely source of stress 
affecting benthic communities in these segments. Sites with posterior probabilities 
of membership in the contaminant group that were greater than 0.50 were classified 
as putatively contaminated. 

3.5. INSUFFICIENT AND EXCESSIVE ABUNDANCE OR BIOMASS 

Insufficient and excessive abundance or biomass was determined from the abun¬ 
dance and biomass metric scores for all sites not classified as putatively 


appendix k 


2006 303(d) Assessment Methods tor Chesapeake Bay Benthos 


K-7 


contaminated. In the B-IBI, a score of 1 is assigned to total species abundance and 
total biomass if the value of these metrics for the site being evaluated is below the 
5th percentile or above the 95th percentile of corresponding reference values. A 
score of 1 is assigned for both insufficient and excessive abundance or biomass 
because abundance and biomass of organisms respond bimodally to pollution. An 
increase in abundance or biomass is expected at polluted sites when stress from 
pollution is moderate, such as at sites where there is organic enrichment of the sedi¬ 
ment. Excessive abundance and excessive biomass are phenomena usually 
associated with eutrophic conditions. A decrease in abundance and biomass is 
expected at sites with high degrees of stress from pollution; for example, sites 
affected by low dissolved oxygen. The insufficient and excessive abundance or 
biomass criteria can then be used to determine the likelihood of low dissolved 
oxygen problems versus eutrophic conditions for each of the Chesapeake Bay 
segments evaluated. 


4.0 RESULTS 

4.1. DEGRADED AREA 

Based on the bootstrap-degraded area procedure, 22 segments with sample size of at 
least 10 were considered impaired (Table 3). Impaired segments were sorted according 
to the lower 95% bound of the confidence interval of the difference between the 
proportion of sites in the segment below threshold (P) and the proportion of sites below 
threshold under the null hypothesis (Po), from high to low. The estimated P for the 
impaired segments ranged from 28 to 76%, and the average B-IBI score was below 3.0 
for most segments (Table 3). The estimates for CB4MH and CB5MH exclude the deep 
trough (>12 m) of the mainstem which is not monitored because this area is subjected 
to summer anoxia and has consistently be found to be azoic. 

Nineteen of the segments declared impaired in this assessment were also declared 
impaired by the Wilcoxon test in the 2004 assessment. Three segments (JMSMHb, 
PMKOHa, MOBPHa) were declared impaired in this assessment but not in the 2004 
assessment, and seven segments (LAFMHa, POCMH, POTOH, GUNOH, TANMH, 
NANMH, CB7PHa) were declared impaired in the 2004 assessment but not in the 
current assessment. Of the new impaired segments, the Nansemond River 
(JMSMHb) and Mobjack Bay (MOBPHa) were sampled with additional effort in 
2004. Previously, these two segments and the Pamunkey River (PMKOHa) had 
sample size <10. Of the segments that are no longer classified as impaired, only the 
Pocomoke River mesohaline (POCMH) had sample size <10 in the current assess¬ 
ment. 

4.2. WILCOXON 

The stratified Wilcoxon rank sum test identified 27 segments with sample size of at 
least 10 as impaired (Table 3). Segments impaired by the Wilcoxon test but not 
impaired by the Degraded Area method were the lower Bay meainstem (CB7PHa), 
Tangier Sound (TANMH), the Lafayette River (LAFMHa), Severn River (SEVMH), 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



K-8 


and Gunpowder River (GUNOH). Except for the Severn River, these segments were 
also identified as impaired in the 2004 assessment. 

4.3. DIAGNOSTIC TOOL AND INSUFFICIENT AND EXCESSIVE 
ABUNDANCE OR BIOMASS 

The diagnostic tool and the insufficient and excessive abundance/biomass criteria 
can be used as ancillary information to determine most likely source of stress 
affecting benthic communities in segments classified as impaired. The results of this 
part of the assessment should be used only as a screening tool to identify probable 
causes of degradation and to prioritize segments for further study. 

There is always a risk of misclassifying sites as affected by toxic contamination, low 
dissolved oxygen, or nutrient enrichment, so independent measurements of sediment 
and water quality should be made whenever possible. Table 4 presents the results of 
the diagnostic tool and the insufficient and excessive abundance/biomass character¬ 
ization for sites with contaminant group posterior probabilities >=0.50, and Table 5 
presents the results for sites with contaminant group posterior probabilities >=0.90. 
A general decision tree for segment assessment and characterization is provided in 
Figure 1. Results are summarized below. 

James River 

The percentages of degraded samples with a contaminant effect ranged from 67% in 
the upper James River (JMSTFa) to 78% in the middle James River (JMSOHa) for 
P >=0.5, with average contaminant group posterior probabilities ranging from 0.64 
to 0.79. At P >=0.9 contaminant percentages ranged from 33-50% (Table 4). At the 
James River mouth (JMSPHa) no samples were classified as contaminated. In addi¬ 
tion, an examination of all samples collected indicated that only one sample had 
excessive abundance/biomass and only one had insufficient abundance/biomass. In 
the Nansemond River (JMSMHb), 90% of the degraded samples were classified as 
contaminated with an average contaminant group posterior probability of 0.87. 
Eighty percent of degraded samples had contaminant group posterior probabilities of 
at least 0.90. Only three samples were collected in the Chuckatuck River/Pagan 
River segment (JMSMHc), and three in the Warwick River (JMSMHd). Although the 
low number of samples makes reliable assessments difficult, degraded samples were 
collected in both segments and each was classified as contaminated with high poste¬ 
rior probabilities of contaminant group membership. Although only three samples 
were collected in Willoughby Bay (JMSPHd), each sample was classified as contam¬ 
inated. Contaminated samples in this segment had an average contaminant group 
posterior probability of 0.84. Additional samples are required in these segments to 
determine the extent of benthic degradation and potential sources of stress. 

In summary, results indicate that contaminants may account for a large portion of the 
degradation in the James River, except for the James River mouth. The primary 
source of degradation in the Nansemond River appears to be anthropogenic contam¬ 
ination. Sampling was not sufficient for a reliable assessment in the Chucktuck/ 
Pagan River and Warwick River segments. 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-9 


Elizabeth River 

Percentages ot degraded samples with a contaminant effect ranged from 50% in the 
lower Elizabeth River mainstem (ELIPHa) to nearly 91% in the Eastern Branch 
(EBEMHa). At least 80% of degraded samples were classified as contaminated in 
both the Southern Branch (SBEMHa) and the Lafayette River (LAFMHa) and 68% 
were classified as contaminated in the upper Elizabeth River mainstem (ELIMHa). 
Of the remaining degraded samples without a contaminant effect, excessive abun¬ 
dance/biomass was found in 9.1%, 12.5%, and 5.3% in the Western Branch 
(WBEMHa), Southern Branch (SBEMHa) and upper Elizabeth River mainstem 
(ELIMHa), respectively, indicating the potential of stress due to eutrophication. 
Only one sample had excessive abundance in the lower Elizabeth River mainstem 
(ELIPHa). Insufficient abundance/biomass was found in 12.5%, 5.9%, and 15.8% of 
the degraded samples without a contaminant effect in the Southern Branch 
(SBEMHa), the Lafayette River (LAFMHa) and the upper Elizabeth River 
(ELIMHa), respectively, indicating low dissolved oxygen as an additional source of 
stress to benthic communities in these segments. 

In summary, the predominant source of stress to benthic communities within the 
Elizabeth River is anthropogenic contamination. Both eutrophication and low 
dissolved oxygen appear to be additional sources of stress within the Southern 
Branch (SBEMHa) and upper Elizabeth River mainstem (ELIMHa). 

York River 

None of the upper Pamunkey River (PMKTF) samples had B-IBI scores <2.7, so 
none were assessed by the diagnostic tool. Over 57% of the lower Pamunkey River 
(PMKOH) degraded samples were classified as contaminated by the tool, but the 
average contaminant group posterior probability was low at 0.62. One additional 
sample in this last segment was not classified as contaminated and had insufficient 
abundance/biomass. Few samples were degraded in the upper Mattaponi River 
(MPNTFa), and 67% of these were classified as contaminated. However, the average 
contaminant group posterior probability was low at 0.65 and no samples collected 
had a probability of contaminant group membership >=0.90. No samples were clas¬ 
sified as having excessive or insufficient abundance/biomass within this segment. In 
the lower Mattaponi River (MPNOHa) 80% of the degraded samples were classified 
as contaminated. The average contaminant group posterior probability in this 
segment was high at 0.87 and group membership probabilities for all samples clas¬ 
sified as contaminated were >=0.90. No uncontaminated degraded samples had 
excessive or insufficient abundance/biomass. In the middle York River (YRKMHa) 
64% of the degraded samples were classified as contaminated. An additional 9.1% 
of degraded samples had excessive abundance/biomass and were not classified as 
contaminated by the tool, while 12.1% of the uncontaminated degraded samples had 
insufficient abundance/biomass. In the lower York River (YRKPHa) only 46% of the 
degraded samples were classified as contaminated. An additional 9.1% and 27.3% of 
uncontaminated degraded samples were found with excessive abundance/biomass 
and insufficient abundance/biomass, respectively, in this segment. In Mobjack Bay 
(MOBPHa), 50% of the degraded samples were classified as contaminated, all with 
contaminant group posterior probabilities >=0.90. An additional 12.5% and 25% of 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-10 


uncontaminated degraded samples were found with excessive abundance/biomass 
and insufficient abundance/biomass, respectively. Insufficient sample size in Severn 
Creek (MOBPHe), Ware River (MOBPHf), and East River (MOBPHh), precluded 
reliable assessments of degradation within these segments. 

In summary, contaminants are likely to be substantial contributors to benthic 
community degradation in the York River, particularly in the lower Mattaponi River 
(MPNOHa) and the middle York River (YRKMHa). Contamination sources of stress 
are unlikely in both the lower York River (YRKPHa) and Mobjack Bay (MOBPHa), 
but both eutrophication and low dissolved oxygen may affect benthic communities 
in these segments, as well as in the lower York River (YRKMHa). 

Rappahannock River 

All of the degraded samples in the upper Rappahannock River (RPPTFa) were clas¬ 
sified as contaminated. Only five samples were collected in the middle 
Rappahannock River (RPPOH), making assessments of benthic community degra¬ 
dation unreliable. In the lower Rappahannock River (RPPMHa), 67% of the 
degraded samples were classified as contaminated, with an average contaminant 
group posterior probability of 0.67. The remaining degraded samples that were not 
classified into the contaminant group had insufficient abundance/biomass. Only 
eight samples were collected in the Corrotoman River. One of these samples was 
classified as contaminated and another as uncontaminated with insufficient abun¬ 
dance/biomass. 

In summary, degradation in the upper Rappahannock River (RPPTFa) appears to be 
the result of anthropogenic contamination while degradation in the lower Rappa¬ 
hannock River may be the result of a combination of contamination and low 
dissolved oxygen effects. The small number of samples collected makes assessments 
of overall benthic community condition in the middle Rappahannock River 
(RPPOHa) and Corrotoman River (CRRMHa) difficult but, the degradation observed 
appears to be from a variety of sources in both segments. 

Potomac River 

Fifty percent of the degraded samples in the upper Potomac River (POTTF) were 
classified as contaminated by the diagnostic tool. None of the uncontaminated 
degraded samples had excessive or insufficient abundance/biomass. In the middle 
Potomac River (POTOH), 80% of the degraded samples were classified as contami¬ 
nated. Of the uncontaminated degraded samples, 20% had excessive 
abundance/biomass and none had insufficient abundance/biomass. In the lower 
Potomac River (POTMH), 31% of the degraded samples were classified as contam¬ 
inated. Of the remaining degraded samples classified as uncontaminated, 65% had 
insufficient abundance/biomass while only 2.6% had excessive abundance/biomass. 

In summary, benthic community degradation in much of the upper Potomac River 
(POTTF) appears to be the result of anthroprogenic contamination. In the middle 
Potomac River (POTOH), the primary source of stress appears to be contamination; 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-11 


however, eutrophication is likely to also affect benthic communities in this segment, 
as indicated by the samples with excessive abundance/biomass. 

The predominant source of stress in the lower Potomac River (POTMH) appears to 
be from low dissolved oxygen, as indicated by the high percentage of samples clas¬ 
sified as uncontaminated and having insufficient abundance/ biomass. 

Patuxent River 

An inadequate number of samples were collected in the upper Patuxent River 
(PAXTF) and middle Patuxent River (PAXOH) for assessing benthic community 
degradation using the benthic diagnostic tool. In the upper Patuxent River (PAXTF), 
two samples were classified as contaminated and one had excessive 
abundance/biomass without likelihood of contamination. In the middle Patuxent 
River (PAXOH), three samples were classified as contaminated and none had exces¬ 
sive or insufficient abundance/biomass. In the lower Patuxent River (PAXMH), 46% 
of the degraded samples were classified as contaminated, with an average posterior 
probability of contaminant group membership of 0.51. Of the remaining uncontam¬ 
inated samples, 50% had insufficient abundance/biomass while only 1.5% had 
excessive abundance/biomass. 

In summary, accurate assessment of benthic community degradation in the upper 
Patuxent River (PAXTF) and middle Patuxent River (PAXOH) requires additional 
sampling; however, available data suggest contaminants may be a source of stress in 
these segments. Degradation in the lower Patuxent River (PAXMH) is likely to be 
the result of a combination of contamination and low dissolved oxygen stress. 

Chester River 

Over 38% of the degraded samples in the lower Chester River (CHSMH) were clas¬ 
sified as contaminated. Of the remaining uncontaminated samples, 11% had 
excessive abundance/biomass and 33% had insufficient abundance/biomass. Benthic 
community degradation in this segment would appear to be the result of contamina¬ 
tion, eutrophication, and low dissolved oxygen effects. All other segments in the 
Chester River had low sample size. 

Choptank River 

Accurate assessment of benthic degradation the upper Choptank River (CHOTF), 
middle Choptank River (CHOOH) and Choptank River mouth (CHOMH1) will 
require additional sampling. In the lower Choptank River (CHOMH2), 67% of the 
degraded samples were classified as contaminated, with group membership proba¬ 
bilities >0.90. Of the remaining uncontaminated degraded samples, 22% had 
excessive abundance/biomass while 11% had insufficient abundance/biomass. Cont¬ 
amination appears to account for most of the benthic community degradation in the 
lower Choptank River (CHOMH2), but eutrophication and low dissolved oxygen are 
also likely to play a role. 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-12 


Pocomoke River 

Pocomoke River segments had low sample size; however, most of the degraded 
samples in the lower Pocomoke were classified as contaminated. 

Pocomoke Sound 

Again, Pocomoke Sound had low sample size; however, 75% of the degraded 
samples were classified as contaminated by the benthic diagnostic tool. Twenty-five 
percent of the uncontaminated samples had insufficient abundance/biomass. Results 
suggest that benthic community degradation in this segment stems from a combina¬ 
tion of contaminants and low dissolved oxygen. 

Manokin River 

Of the Maryland small Eastern Tributaries, only the Manokin River (MANMH) had 
adequate sample size. Seventy-five percent of the degraded samples were classified 
as contaminated. Of the remaining uncontaminated and degraded samples, 25% had 
insufficient abundance/biomass. 

Maryland Upper Western Tributaries 

In the Gunpowder River (GUNOH), only 17% of the samples were classified as 
contaminated. Of the uncontaminated samples, 50% had insufficient 
abundance/biomass and another 17% had excessive abundance/biomass. The 
predominant source of stress to benthic communities in this segment appears to be 
low dissolved oxygen. In the Magothy River (MAGMH), 38% of the degraded 
samples were classified as contaminated. Excessive abundance/ biomass was 
observed in 13% and insufficient abundance/biomass in 50% of the uncontaminated 
degraded samples. Results suggest a mixed source of stress. In the Patapsco River 
(PATMH), 58% of the degraded samples were classified as contaminated. The 
remaining degraded samples had insufficient abundance/biomass, suggesting 
contaminants and low dissolved oxygen as sources of stress. In the Severn River 
(SEVMH), 60% of the degraded samples were classified as contaminated. An addi¬ 
tional 20% and 40% of the uncontaminated degraded samples had excessive and 
insufficient abundance/biomass, respectively. Results suggest a variety of sources of 
stress for this segment. 

Chesapeake Bay Mainstem 

Sixty-seven percent of the upper Chesapeake Bay (CB1TF) degraded samples had 
possible contaminant effects, and 17% of the remaining degraded samples had 
excessive abundance/biomass. Segment CB20H, on the other hand, had no degraded 
samples. In Segment CB3MH, 55% of the degraded samples were classified as 
contaminated while 32% of the remaining degraded samples had insufficient abun¬ 
dance/biomass. In Segment CB4MH, 35% of the degraded samples were classified 
as contaminated, 25% of the uncontaminated degraded samples had excessive 


appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-13 


abundance/biomass, and 35% had insufficient abundance/biomass. Few samples in 
Tangier Sound were degraded. In Segment CB5MH, 18% of degraded samples were 
classified as contaminated and 82% of the uncontaminated degraded had insufficient 
abundance/biomass, indicating a low dissolved oxygen effect. In the lower main- 
stem, Segment CB6PH had 67% of the degraded samples classified as contaminated 
and 33% of the uncontaminated degraded samples classified with insufficient abun¬ 
dance/biomass. Segment CB7PHa had 63% of the degraded samples classified as 
contaminated, but none had contaminant group posterior probabilities above 0.90 
and the average probability for the segment was 0.58. Of the degraded samples not 
classified as contaminated in this last segment, 13% had excessive abundance/ 
biomass and 25% had insufficient abundance/biomass. Finally, none of the samples 
near the Bay mouth in Segment CB8PHa were classified as contaminated. 

In summary, contaminants were likely sources of stress to benthic communities in 
CB1TF and CB3MH, while a variety of stresses were likely in CB4MH. Low 
dissolved oxygen was the predominant source of stress in CB5MH, contaminants 
and low dissolved oxygen in CB6PHa and CB7PHa, and low dissolved oxygen alone 
in CB8PHa. 


5.0 REFERENCES 

Alden, R.A. Ill, D.M. Dauer, J.A. Ranasinghe, L.C. Scott, and R.J. Llanso. 2002. Statistical 
verification of the Chesapeake Bay benthic index of biotic integrity. Environmetrics 13:473- 
498. 

Cytel Software Corporation. 2002. Pro-StatXact for SAS users. Statistical Software for Exact 
Non-Parametric Inference. 

Dauer, D.M., M.F. Lane, and R.J. Llanso. 2002. Development of Diagnostic Approaches to 
Determine Sources of Anthropogenic Stress Affecting Benthic Community Condition in the 
Chesapeake Bay. Prepared for U.S. Environmental Protection Agency, Chesapeake Bay 
Program Office, by Department of Biological Sciences, Old Dominion University, Norfolk, 
VA. 

Dauer, D.M., M.F. Lane, and R.J. Llanso. 2005. Addendum to the Report: Development of 
Diagnostic Approaches to Determine Sources of Anthropogenic Stress Affecting Benthic 
Community Condition in the Chesapeake Bay. Prepared for U.S. Environmental Protection 
Agency, Chesapeake Bay Program Office, by Department of Biological Sciences, Old 
Dominion University, Norfolk, VA„ and Versar, Inc., Columbia, MD. 

Efron, B. and R. Tibshirani. 1998. An Introduction to the Bootstrap. Chapman & Hall/CRC. 

Llanso, R.J., J.H. Vplstad, and D.M. Dauer. 2003. Decision Process for Identification of 
Estuarine Benthic Impairments. Prepared for Maryland Department of Natural resources. 
Tidewater Ecosystem Assessments, Annapolis, MD., by Versar, Inc., Columbia, MD., and 
Department of Biological Sciences, Old Dominion University, Norfolk, VA. 

Schenker, N. and J.F. Gentleman. 2001 . On judging the significance of differences by exam¬ 
ining the overlap between confidence intervals. The American Statistician 55:182-186. 

TMWA (Tidal Monitoring and Analysis Workgroup). 1999. Chesapeake Bay Program. 
Analytical Segmentation Scheme for the 1997 Re-evaluation and Beyond. Prepared for the 
U.S. Environmental Protection Agency, Chesapeake Bay Program Office, by the Tidal Moni- 


appenclix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



K-14 


toring and Analysis Workgroup of the Chesapeake Bay Program Monitoring and Assessment 
Subcommittee, Annapolis, MD. 

Weisberg, S.B., J.A. Ranasinghe, D.M. Dauer, L.C., Schaffner, R.J. Diaz, and J.B. Frithsen. 
1997. An estuarine benthic index of biotic integrity (B-IB1) for Chesapeake Bay. Estuaries 
20:149-158. 


appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 


K-15 


Table 1. Habitat classification for the Chesapeake Bay B-1 B1. 


Habitat Class 

Bottom Salinity (psu) 

Silt-clay (<62 p) content by 
Weight (%) 

1 . 

Tidal freshwater 

0-0.5 

N/A 

2. 

Oligohaline 

>0.5-5 

N/A 

3. 

Low mesohaliue 

>5-12 

N/A 

4-1. 

High mesohaline sand 

>12-18 

0-40 

4-2. 

High mesohaline mud 

>12-18 

>40 

5-1. 

Polyhaline sand 

>18 

0-40 

5-2. 

Polyhaline mud 

>18 

>40 


Table 2. Number of samples by habitat in the original index development data files used by Weisberg et al. (1997) and 
Alden et al. (2002). Calibration (Cal) and validation (Val) samples combined. Habitat Class designations as in 
Table 1. 



Habitat Class 


1 

2 

3 

4-1 

4-2 

5-1 

5-2 

Cal + Val 








Reference Degraded 

136 

92 

49 

5 

81 

7 

136 

Reference Good 

75 

32 

20 

14 

39 

39 

24 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 





















K-16 



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2006 303(d) Assessment Methods for Chesapeake Bay Benthos 













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Table 4. Diagnostic assessment of benthic community degradation for random sites sampled within Chesapeake Bay seg- 


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2006 303(d) Assessment Methods for Chesapeake Bay Benthos 










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2006 303(d) Assessment Methods for Chesapeake Bay Benthos 










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2006 303(d) Assessment Methods for Chesapeake Bay Benthos 










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appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 























































K-35 


APPENDIX 

POWERPOINT PRESENTATION 


Benthic Index of Biotic Integrity 
(B-IBI) for 2006 303(d) List 

Alternative Assessment 
Methodology 


Roberto Llanso, Jon Volstad, Ed Weber 
Versar. Inc. 

Daniel Dauer 
Old Dominion University 
(co-PIs) 

August 23. 2005 


Summary 

The impairment assessment for each segment is based on the 
proportion of samples with ' low" B-IBI scores (i.e.. below a 
threshold) 

Two steps, estimate: 

1 Proportion of sites in a segment with scores below a threshold (P) 

2 Difference between P and the expected proportion under the null 
hypothesis (P c ), i e., if the segment were in good condition (no low 
DO, contaminant, or nutrient enrichment problems), we would still 
expect a small proportion of sites to have “low" scores (e g., because 
of natural variability) this proportion under the null hypothesis is 
defined as 5% 


appendix k - 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 






K-36 


Summary (cont.) 


Thresholds are set for each of seven benthic habitats in Chesapeake 
Bay: tidal fresh oligohaline. low mesohaline high mesohaltne sand, high 
mesohaline mud. polyhalme sand, polyhaline mud. 

The threshold is set as the smaller of two values: 

1. 5 Th percentile IBI score for the good reference distribution (i.e, sites 
with low scores are unlikely to come from good reference conditions) 

2 Maximum observed IBI score for the degraded reference distribution 
(i.e, sites with low scores are likely to come from degraded 
conditions) 

See example next slide for two hypothetical habitats 1) Habitat A, the 
distributions of scores for the good and the degraded reference sites do 
not overlap, 2) Habitat B, the distributions overlap 


Habitat A 


EEE2 



IBI scores 


20 2.7 




IBI scores 


T 


2.2 30 


appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 












K-37 


Summary (cont.) 


• Reference distributions are sometimes based on a small number of 
samples; therefore the 5 lh percentile score is not well defined 

• The S'* percentile score and its variance was estimated by bootstrap 
simulations 

• For each iteration of the bootstrap simulation, a subset of the good 
reference data for each habitat was selected at random, and the 5 11 
percentile score determined 

• Over all the iterations, the 5* 1 percentile score varied, and at each iteration 
the threshold was established according to the rule described earlier 

• See next slide for the two habitat examples 



appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



















K-38 


Summary (cont.) 

• For each iteration of the bootstrap simulation, the assessment data are 
compared to the reference data to estimate proportion of sites with scores 
below the threshold 

• This is done for each of one or more habitats wilhin a segment (i e . some 
segments have sites in more than one habitat) 

• See next slides for the two examples 




£ 

§ 

Cr 

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/ 


/ 


IBI scores 



2.0 


2 . 7 - 

5 % 


3 3 


9 


* 

♦ a* • 


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• Habitat B 


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. * « 



appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 















K-39 


Hah lat B 




• 

Summary (cont.) 

Example of calculations for a hypothetical segment with two habitats: 


Iteration 

n 

Habitat A 

Ihreshold P <thr©shold 

n 

Habitat B 

threshold P threshold 

P < threshold for A ♦ B’ 

1 

to 

2 0 0/4C 

40 

2 2 0.30 

32.0 


2 

10 

2 0 0.40 

40 

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3 

10 

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40 

1 7 0.28 

304 













n 

10 

2 0 0.4C 

40 

3 0 0.48 

45.4 


P total < threshold = 

Average * SE 


•(n 3 , ♦ nP yjr, ♦ n t ). expressed as percent 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 

























K-40 


Summary (cont.) 

• Under the null hypothesis, 5% of the sites (P 0 ) would be expected to have 
low IBI scores, even if all sites in a segment were in good condition (i e, no 
low DO, contaminant, nutrient enrichment problems) 



f ** ^ 

: ' V /. - \ 


• Segments declared impaired if P greater than expected under the null 
hypothesis 

P - P > 0 (with 95% confidence) 


Summary (cont.) 

• Variance components in P added 

• Variance in P due to estimating thresholds - from bootstrap 

• Sampling variation within segment - binomial 

• Confidence interval of P - P 0 = 

P-P 0 ± 1.96(SE P + SE P ) = P - P 0 * 1 96*SQRT(Var p * Var p J 

i ( p _ ~p\- 

Var,, = Variance from bootstrap = V -r Plus variance 

1 5CXK) — l 

from segment = (pq/N-1) 


appendix k • 2006 303(d) Assessment Methods for Chesapeake Bay Benthos 







K-41 


Advantages of new method over Wilcoxon’s 

Wilcoxon 

• evaluates differences in distributions based on ranks, cannot quantify 
magnitude of shift 

• sensitive to small shifts in distribution of B-lBI scores 

New method 

• estimates proportion of area below thresholds and magnitude of 
departure from reference conditions 

• tests if this magnitude is above specific thresholds of protection 

• incorporates uncertainty in reference conditions as well as sampling 
variability in the assessment data 

• does not require purchase of special statistical analysis package 
(Wilcoxon does) 

• Both methods are suitable for data segregated into multiple habitats for 
which reference distributions are not homogeneous 


appendix k 


2006 303(d) Assessment Methods for Chesapeake Bay Benthos 



L-1 


a ppend ix 


I 


Addendum to the Report 

Development of Diagnostic Approaches to 
Determine Sources of Anthropogenic Stress 
Affecting Benthic Community Conditions 
in the Chesapeake Bay 


Prepared for: 


U.S. EPA Chesapeake Bay Program Office 
410 Severn Avenue, Suite 109 
Annapolis, Maryland 21403 


Prepared by: 

Daniel M. Dauer 1 
Michael F. Lane 1 
Roberto J. Llanso 2 


'Department of Biological Sciences 
Old Dominion University 
Norfolk, VA 23529-0456 

2 Versar, Inc. 

9200 Rumsey Road 
Columbia, Maryland 21045 


June 2005 


appendix I 


Addendum to the Report 


L-2 


1. INTRODUCTION 

Dauer et al. (2002) submitted a report to the US EPA Chesapeake Bay Program 
Office on the development of diagnostic approaches to determine sources of anthro¬ 
pogenic stress affecting benthic community condition in the Chesapeake Bay. The 
objective of the study was to develop analytical tools capable of classifying regions 
in Chesapeake Bay identified as having degraded benthic communities into cate¬ 
gories distinguished by the type of stress experienced by those communities. The 
tool was successful at identifying regions with high probabilities of sediment 
contamination. However, prior to implementation, it was recommended that the 
operational effectiveness of the diagnostic tool be further tested using additional 
validation data sets. 

In this Addendum the results of two additional tasks are presented. First, the linear 
discriminant function was independently derived to verify the accuracy of the devel¬ 
opment of the function. Second, two additional putative validation data sets were 
used to assess the validity of the linear discriminant function. 


2. LINEAR DISCRIMINANT FUNCTION 

In this task it was discovered that four samples from the original calibration data set 
were not included in the derivation of the final linear discriminant function originally 
reported in Dauer et al. 2002. The final validation of the linear discriminant func¬ 
tion with these additional four samples was identical to that reported in Table 21 for 
the Baywide scenario, i.e. using the All Province sediment contaminant classifica¬ 
tion, namely, with an overall percent correct classification of 75.14%. The new 
coefficients for this function are given in Table 1 of this Addendum (revised Table 
24 of Dauer et al. 2002). 


3. ADDITIONAL VALIDATION DATA SETS 

Two putative data sets were used for further validation of the Contaminant Discrim¬ 
inant Tool (CDT) as presented in Dauer et al. 2002. 

ELIZABETH RIVER WATERSHED 

The first putative data set consisted of 125 random samples collected in 1999 from 
the Elizabeth River watershed (Dauer and Llanso 2003). An additional 100 random 
samples collected 25 per year from 2000-2003 were also used (Dauer 2001, 2002, 
2003, 2004). All samples were analyzed using the CDT function and placed into 
categories based upon the posterior probability of inclusion into the Contaminant 
Group. Due to the high levels of contaminants recorded historically in the Elizabeth 
River watershed (Hall et al., 1992, 1997, 2002; Padma et al. 1998; Conrad et al. 
2004), the a priori expectation was that a high percentage of samples declared 
degraded by the Benthic Index of Biotic Integrity would be placed into the Contam¬ 
inant Group. The results from the Elizabeth River watershed are compared to results 


appendix 


Addendum to the Report 





L-3 


from the Virginia Mainstem that is characterized as having low levels of contami¬ 
nants and accordingly classified as of no environmental concern (USEPA 1999). 

Our a priori expectation was that all branches of the Elizabeth River would show a 
higher percent area placed into the Contaminant Group compared to the Virginia 
Mainstem. For the Virginia Mainstem the number of sites placed into the Contami¬ 
nant Group represented 11% of the entire stratum. Consistent with our a priori 
expectation, all strata in the Elizabeth River had higher proportions placed into the 
Contaminant Group, ranging from 40-92% (Table 2; Figure 1). These results indi¬ 
cate strong support for the CDT. 

1996-2002 RANDOM DATA FOR CHESAPEAKE BAY 

The second putative data set consisted of random samples collected as part of the 
Maryland and Virginia Benthic Monitoring Program from 1996-2002. All samples 
were analyzed using the CDT function and placed into categories based upon the 
posterior probability of inclusion into the sediment Contaminant Group. The a 
priori expectation was that more samples collected near highly urbanized or indus¬ 
trialized watersheds would be placed into the Contaminant Group. Results are more 
difficult to interpret but the pattern of location of samples placed into the Contami¬ 
nant Group is non-random (Table 3; Figure 2), and can be interpreted to be consistent 
with known patterns of sediment contaminant distributions for the entire Chesapeake 
Bay (e.g. see USEPA 1999). GIS maps show patterns of location that agree well 
with a priori expectations within highly contaminated regions of the Bay such as 
Baltimore Harbor (Figure 3) and the Elizabeth River (Figure 4). The maps were 
made with data placed on a 100 m grid and interpolated using a two-dimensional 
surface fitting algorithm. 


4. REFERENCES 

Conrad, C.F. and C.J. Chisholm-Brause. 2004. Spatial survey of trace metal contaminants 
in the sediments of the Elizabeth River, Virginia. Marine Pollution Bulletin 49:319324. 

Dauer, D.M. 2001. Benthic Biological Monitoring Program of the Elizabeth River Water¬ 
shed (2000). Final Report to the Virginia Department of Environmental Quality, Chesapeake 
Bay Program, 35 pp. plus Appendix. 

Dauer, D.M. 2002. Benthic Biological Monitoring Program of the Elizabeth River Water¬ 
shed (2001) with a study of Paradise Creek. Final Report to the Virginia Department of 
Environmental Quality, Chesapeake Bay Program, 45 pp. 

Dauer, D.M. 2003. Benthic Biological Monitoring Program of the Elizabeth River Water¬ 
shed (2002). Final Report to the Virginia Department of Environmental Quality, Chesapeake 
Bay Program, 56 pp. 

Dauer, D.M. 2004. Benthic Biological Monitoring Program of the Elizabeth River Water¬ 
shed (2003). Final Report to the Virginia Department of Environmental Quality, Chesapeake 
Bay Program, 88 pp. 

Dauer, D.M., M.F. Lane and R.J. Llanso. 2002. Development of Diagnostic Approaches to 
Determine Sources of Anthropogenic Stress Affecting Benthic Community Condition in the 


appendix 1 


Addendum to the Report 



L-4 


Chesapeake Bay. Final Report to the U.S. Environmental Protection Agency, Chesapeake 
Bay Program Office, Annapolis, Maryland, 64 pp. 

Dauer. D.M. and R.J. Llanso. 2003. Spatial scales and probability based sampling in deter¬ 
mining levels of benthic community degradation in the Chesapeake Bay. Environmental 
Monitoring and Assessment 81:175-186. 

Hall, L.W. Jr. and R.W. Alden. III. 1997. A review of concurrent ambient water column and 
sediment toxicity testing in the Chesapeake Bay watershed: 1990-1994. Environmental 
Toxicology and Chemistry 16:16061617. 

Hall, L.W. Jr.. R.D. Anderson and R.W. Alden, III. 2002. A ten-year summary of concurrent 
ambient water column and sediment toxicity tests in the Chesapeake Bay watershed: 
19901999. Environmental Monitoring and Assessment 76:311352. 

Hall, L.W. Jr., M.C. Ziegenfuss and S.A. Fischer. 1992. Ambient toxicity testing in the 
Chesapeake Bay watershed using freshwater and estuarine water column tests. Environ¬ 
mental Toxicology and Chemistry 11:14091425. 

Padma, T.V., R.C. Hale, and M.H. Roberts. 1998. Toxicity of water-soluble fractions derived 
from whole creosote and creosote-contaminated sediments. Environmental Toxicology and 
Chemistry 17:16061610. 

USEPA. 1999. Targeting Toxics: A Characterization Report. A Tool for Directing Manage¬ 
ment and Monitoring Actions in the Chesapeake Bay’s Tidal Rivers, 1999. U.S. 
Environmental Protection Agency, Chesapeake Bay Program Office, Annapolis, Maryland, 
49 pp. 


appendix I 


Addendum to the Report 


L-5 


Table 1. Revised Table 24 of Dauer et al. (2002). Coefficients and cutoff values for the Baywide linear discriminant 
function for classifying severely degraded and degraded sites into the Contaminant and Other stress 
groups using "uncorrected" data. 


Variable 

Coefficient 

Variable Coefficient 

Isopoda abundance 

2.01518 

Nereidae abundance 

-0.28511 

Isopoda diversity 

-3.07226 

Nereidae richness 

-0.53535 

Isopoda proportional abundance 

9.45420 

Nereidae proportional abundance 

12.23099 

Amphipoda abundance 

0.38084 

Oligochaeta abundance 

0.43911 

Amphipoda richness 

-0.32010 

Oligochaeta richness 

1.37409 

Amphipoda proportional abun. 

-4.25029 

Oligochaeta proportional abundance 

-5.05367 

Haustoriidae abundance 

-3.85522 

Tubificidae abundance 

0.33669 

Haustoriidae diversity 

-1.39235 

Tubificidae richness 

0.96057 

Haustoriidae proportional abun. 

34.61687 

Tubificidae proportional abundance 

-2.27273 

Ampeliscidae abundance 

-1.57316 

Deep deposit feeder abundance 

-1.07320 

Ampeliscidae richness 

-1.79716 

Deep deposit feeder richness 

-2.43057 

Ampeliscidae proportional abun. 

25.88958 

Deep deposit feeder proportional abun. 

12.57963 

Corophiidae abundance 

37.26499 

Suspension feeder abundance 

1.05255 

Corophiidae richness 

-18.36548 

Suspension feeder richness 

-1.25065 

Corophiidae proportional abun. 

-2329.15377 

Suspension feeder proportional abun. 

2.17966 

Mollusca abundance 

2.52241 

Interface feeder abundance 

0.84134 

Mollusca richness 

0.74909 

Interface feeder richness 

-0.47052 

Mollusca proportional abundance 

-1.43165 

Interface feeder proportional abundance 

4.50630 

Bivalvia abundance 

.4.43466 

Carnivore-Omnivore abundance 

-0.05179 

Bivalvia richness 

1.28499 

Carnivore-Omnivore richness 

-0.00602 

Bivalvia proportional abundance 

-0.27727 

Carnivore-Omnivore proportional abun. 

3.13784 

Gastropoda abundance 

-1.23734 

Total Abundance 

0.18311 

Gastropoda richness 

-0.15477 

Total biomass 

4.75310 

Gastropoda proportional abun. 

-3.82240 

Biomass to abundance ratio 

123.97124 

Polychaeta abundance 

0.05506 

Infaunal species richness 

-0.04107 

Polychaeta richness 

0.46294 

Infaunal Shannon Wiener diversity 

1.22042 

Polychaeta proportional abun. 

-5.08183 

Infaunal species evenness 

-2.50732 

Spionidae abundance 

-0.02286 

Epifauna to Infaunal abundance ratio 

4.41998 

Spionidae richness 

-1.89087 

Epifauna species richness 

-0.96505 

Spionidae proportional abundance 

4.02486 

Epifaunal Shannon Wiener diversity 

-1.11725 

Capitellidae abundance 

0.48588 

Epifaunal species evenness 

5.85736 

Capitellidae richness 

2.55550 



Capitellidae proportional abun. 

-1.67289 




Cutoff Value = 2.56645 


appendix I 


Addendum to the Report 




L-6 


Table 2. Percent of the Elizabeth River 1999 strata placed into the sediment 

contaminant effect group using the contaminant discriminant function of 
Dauer et al. 2002 (posterior probability > 0.5). Scuffletown, Gilligan, Jones, 
and Paradise creeks are subsystems of the Southern Branch. Paradise Creek 
sampled in 2000. The Elizabeth River strata are compared to the Virginia 
Mainstem Stratum. 


Stratum 

Percentage of Stratum 
in Contaminant Group 

Mainstem of the Elizabeth River 

40 

Lafayette River 

60 

Eastern Branch 

64 

Western Branch 

72 

Southern Branch 

64 

Scuffletown Creek 

60 

Gilligan/Jones Creek 

68 

Paradise Creek (2000) 

92 

Entire Elizabeth River watershed* 

54 

Virginia Mainstem 

11 


* Area weighted value 


> 0.5 

100 



Figure 1. Percentage of stratum with a B-IBI value < 2.7 and placed into the 
Contaminant Group with a posterior probability > 0.5. 


appendix I 


Addendum to the Report 











































L-7 


Table 3. Percent of the stratum placed into the sediment contaminant effect 

group using the contaminant discriminant function of Dauer et al. 2002 
(posterior probability > 0.5). Data from 1996-2002. Elizabeth River data 
includes the intensive 1999 event and 25 random samples of the watershed 
from 2000-2002. 


Stratum 

N 

Percentage of stratum 
in Contaminant Group 

Lower (VA) Mainstem 

175 

10.9 

Upper Bay Mainstem 

175 

17.7 

MD Eastern Tributaries 

175 

16.6 

Patuxent River 

175 

20.0 

MD Middle Mainstem 

175 

17.1 

MD Western Tributaries 

175 

24.6 

Potomac River 

175 

31.4 

James River 

175 

30.9 

Rappahannock River 

175 

37.1 

York River 

175 

38.3 

Elizabeth River 

275 

52.4 


% of stratum 



Figure 2. Percentage of stratum with a B-IBI value < 2.7 and placed into the 
Contaminant Group with a posterior probability > 0.5. 


appendix 


Addendum to the Report 





































L-8 



Figure 3. Diagnostic discriminant tool results and an interpolation fitting algorithm were 
used to classify Baltimore Harbor benthic communities into categories distinguished by 
the type of stress experienced by those communities. Red shading indicates degraded 
benthic communities stressed by toxic contamination (posterior probability in 
Contaminant Group > 0.5), with higher color intensity indicating higher probabilities of 
contaminant effects (>0.5 to <0.7; > = 0.7 to <0.9; > = 0.9). Salmon shading indicates 
degraded benthic communities stressed by other sources, most likely low dissolved oxy¬ 
gen (posterior probability in Contaminant Group <=0.5). Green indicates good benthic 
community condition. Middle Branch (mb), Curtis Creek (cc), Stony Creek (sc), and Bear 
Creek (be) show contamination as likely source of stress. The deep basin north of Curtis 
Bay and the deep channel southwest of Sparrows Point (sp) shows other stress (low DO) 
as probable cause of degradation. 


appendix I 


Addendum to the Report 




LIBRARY OF CONGRESS 



Figure 4. Diagnostic discriminant tool results and an interpolation fitting algorithm used 
here to classify lower James River benthic communities into categories distinguished by 
the type of stress experienced by those communities. Red shading indicates degraded 
benthic communities stressed by toxic contamination (posterior probability in 
Contaminant Group > 0.5), with higher color intensity indicating higher probabilities of 
contaminant effects (>0.5 to <0.7; > = 0.7 to <0.9; >=0.9). Salmon shading indicates 
degraded benthic communities stressed by other sources (posterior probability in 
Contaminant Group < = 0.5). Green indicates good benthic community condition. The 
Elizabeth River (er), Craney Island (ci), Willoughby Bay (wb), Nansemond River (nr), and 
Pagan River (pr) show contamination as likely source of stress. 


appendix I 


Addendum to the Report 






































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